The implications of the global credit crisis for credit scoring models

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The implications of the global credit crisis for credit scoring models
A D&B | DI Whitepaper

                                                   The implications of the
                                                   global credit crisis for
                                                   credit scoring models

                        decisionintellect.com.au                              Page 1
The implications of the global credit crisis for credit scoring models
CONTENTS
                 The global environment has undergone a fundamental change....................... 3

                 Credit scoring – a critical component in effective risk assessment ....................... 5

                 The impact of the global crisis on scoring models............................................. 9

                 Handling the crisis – differences in the preparedness of various sectors . ......... 12

                 Aligning scoring models with risk appetite....................................................... 14

                 Effective utilisation of credit scoring models . ................................................. 20

                 Looking ahead to comprehensive reporting .................................................... 22

                 Strategy optimisation – the next step in customer credit decisioning.................. 26

                 About Decision Intellect.......................................................................................... 28

decisionintellect.com.au                                                                                                        Page 2
The global environment
                   has undergone a
                   fundamental change
                   The global financial crisis has dramatically changed the world we live in. Early International
                   Monetary Fund (IMF) estimates were that losses from the global financial crisis could total
                   in excess of $950 billion. However, this figure is being revised upwards by experts on a
                   regular basis. Sub-prime mortgages, which arguably initiated the crisis, account for fourteen
                   per cent of the US mortgage market1, and although this is a relatively high proportion by
                   global standards, the impact of these products on the global financial system shocked experts
                   around the world. For many, these events have brought into question the credit assessment
                   process, including the role of credit scoring.

                   However it is important to understand that the financial crisis is not the result of ineffective
                   scoring models for US consumer loans – the sub-prime meltdown was simply the catalyst for
                   the unfolding of events. Current global turmoil is the result of consumers and firms allowing
                   booming economic conditions and a desire for profit to outweigh appropriate regard for credit
                   quality. This complacency regarding risk was prevalent across the entire financial system.

                       The global environment is experiencing a fundamental shift – this means existing
                       systems and processes are going to produce different outcomes to those attained prior
                       to the onset of the credit crisis.

                   As a consequence, the credit crisis now dominates the economic outlook for 2009. No region
                   will escape the fall-out and accordingly this year is expected to be the weakest in terms of
                   global economic growth since the early 1990’s.

                   Throughout the crisis, Australian lenders (banking & finance, telecommunications companies
                   and utilities providers) have remained relatively prudent in their provision of credit. Yet despite
                   their prudence, credit has been widely available and ideal debt positions have been surpassed
                   in a number of cases. Government packages have attempted to re-stimulate spending
                   however spare funds and hand-outs have been largely used by consumers and business to
                   pay down debt or to save.

                   Credit and financial risks are now a very real problem in Australia. During 2008 both consumer
                   default rates and commercial insolvencies appointments increased. Economic activity will
                   likely weaken further during 2009 and cash flow problems will become an even greater burden
                   for businesses and consumers. The impact of these trends will continue to flow through to
                   other areas of the economy, causing late payments to become increasingly frequent and
                   bankruptcies to continue rising.

                   1
                       Morris Goldstein, Peterson Institute for International Economics

decisionintellect.com.au                                                                                      Page 3
“The system has changed so fundamentally and rapidly that the normal mechanisms
                     aren’t working as they once did. We have to take a step back and say, we have to create
                     some more bumpers, or whatever we want to call it, in terms of how we look out for our
                     client base.”

                     Jerry Flum, CEO – CreditRiskMonitor

                 To prevent an onset of delinquencies and a substantial increase in bad debt write-off credit
                 managers need to be absolutely certain about the level of risk associated with a transaction
                 and they need to understand how they can alleviate risk from their existing portfolio.

                 The global environment is experiencing a fundamental shift – this means existing systems and
                 processes are going to produce different outcomes to those attained prior to the onset of the
                 credit crisis.

                 Banking executives throughout the world now realise that a lack of discipline in risk
                 management was a significant factor in the credit crisis. However, it has taken a financial
                 meltdown of unprecedented magnitude for organisations to recognise that a strong focus on
                 the fundamentals is absolutely critical no matter what the economic climate2.

                 The key for Australia is to effectively manage the next twelve months.

                 Re-introducing the free flow of credit in a responsible manner is a vitally important step towards
                 reversing the detrimental impacts of the global financial crisis. Effective credit decisioning
                 systems which enable sound lending decisions are a critical part of the solution. Sophisticated
                 credit scoring models and automation are central components in any sound credit risk
                 assessment process and they are the key to survival and profitability in this new and constantly
                 changing environment.

                 This paper examines the impacts of the global credit crisis on scoring models and outlines how
                 those models need to be adjusted to ensure that credit providers avoid an onset of delinquencies
                 and bad debt. It also outlines how credit scoring models can be utilised to improve credit quality
                 and speed of application while reducing costs to the business and considers the implications of
                 potential legislative changes (the Privacy Act) for lenders.

                 2
                     Never again? Risk management in banking beyond the credit crisis. KPMG International, February 2009

decisionintellect.com.au                                                                                                   Page 4
Credit scoring – a critical
                 component in effective risk
                 assessment

                 Credit scoring plays a critical role in the risk assessment process of many organisations, with
                 figures indicating that more than seventy five per cent of mortgage lenders and ninety per
                 cent of credit card providers in the developed world now use credit scores to determine the
                 risk associated with a loan3.

                 Credit scoring systems are widely acknowledged to be superior to the previous method of
                 evaluation (where applications were vetted individually) and are deemed a precise, quantitative
                 way to evaluate repayment risk. Experiences in high-income countries demonstrate that
                 scoring – when properly used – decreases arrears, improves consistency in the decision
                 making process, enables efficiencies and increases profits and client acquisitions4. The
                 uptake of credit scoring has largely been driven by these attributes.

                      Trade payments information reveals how an organisation is paying its current obligation.
                      In a changing or turbulent economy this information is vital to establishing a sound
                      understanding of a company’s financial stability.

                 The development of a credit scorecard is a statistical process which utilises key data. This
                 information is selected on the basis of its ability to determine a particular outcome. In many
                 cases the outcome relates to payment of credit commitments however scorecards can also
                 be utilised to detect incidents such as fraud and churn.

                 In Australia, a consumer score generally takes into account factors such as age, residential
                 status, time at address, employment, occupation and bureau data while a commercial score
                 utilises data elements including organisation size, trading time, court actions, collections,
                 financial data and ratios, trade payment information, director court actions and registered
                 charges.

                 Financial details are an essential element in commercial credit scores due to their ability to
                 predict business failures, particularly for larger sized firms. However, because this information
                 is reported relatively infrequently – the data could be up to twelve months old at the time a
                 decision is made – it is often not available or timely. Trade payments information is reported
                 monthly and consequently, it reveals how an organisation is paying its current obligation. In a
                 changing or turbulent economic climate, this information is vital to establishing a sound and
                 up to date understanding of a company’s financial stability.’

                 3
                     The sense behind credit scoring: a briefing for policy makers. American Financial Services Association 2005
                 4
                     Credit scoring, banks and microfinance: Balancing high-tech with high touch. Hans Dellien & Mark Schreiner 2005

decisionintellect.com.au                                                                                                     Page 5
The characteristics utilised in the application assessment process are similar to those used
                                       in traditional judgmental lending. However, when these characteristics are used in scoring
                                                            The characteristics utilised in          Application               Character
                                       models they are each thereviewed
                                                                applicationindividually
                                                                             assessmentagainst the Purpose
                                                                                                       desiredof loan outcome and
                                                                                                                              Age only included in

                                       the model if they areprocess
                                                             provenareto similar to those
                                                                         be sufficiently   predictive.Deposit                 Time at current address
                                                            used in traditional judgmental          Security               Residential status
                                                            lending. However when these                                    Time at current employment
                                                            characteristics are used in
                                       Scorecard models are typically developed using regression analysis to explain the relationships
                                                            scoring models they are each            Bureau                   Financial
                                       between the individual   characteristics.
                                                            reviewed                The outcome ofBureau
                                                                      individually against           thisscores
                                                                                                             analysis is a model
                                                                                                                             Assets
                                                                                                                                        which utilizes
                                                            the desired
                                       different score variations basedoutcome
                                                                           on the and  only
                                                                                   applicant’s details.
                                                                                                    Bureau negative data     Liabilities
                                                            included in the model if they           Bureau enquiries         Income / Cashflow
                                                            are proven to be sufficiently
                                                            predictive.                                     Sample Scorecard Variables
 he characteristics utilised in             Application                Character
he application assessment                                       Scorecard models are                  Characteristic               Score Increment
                                            Purpose of loan           Age
 rocess are similar to those                                    typically developed using             Age < 21                     -50
                                           Deposit                    Time at current address
                                                                regression analysis to                Age 21-30                    0
 sed in traditional judgmental             Security                   Residential status
                                                                                                      Age >30                      45
                                                                explain the relationships
ending. However when these                                            Time at current employment
                                                                between the individual                Time at Address < 6mths      -30
 haracteristics are used in                                     characteristics. The
                                                                                                      Time at Address 6-12mths     -10
                                                                                                      Time at Address 6-12mths     0
 coring models they are each               Bureau               outcome     of this analysis
                                                                        Financial                     etc
eviewed individually against               Bureau scores        is a model
                                                                        Assetswhich utilizes
he desired outcome and only                Bureau negative data different  score variations
                                                                        Liabilities
ncluded in the model if they               Bureau enquiries     based Income
                                                                        on the/ Cashflow
                                                                                    applicant’s
 re proven to be sufficiently                                   details.                                              Sample Scorecard
 redictive.                                        Sample Scorecard Variables
                                                                Score increments are typically simple score variations (as shown above) which
                                                                simplify the implementation process. However, they can be more complex, with
  corecard models are                Characteristic             the increment       calculated based on the underlying characteristic.
                                                                      Score Increment
ypically developed using             Age < 21                         -50
egression analysis to                AgeScore
                                          21-30 increments are      typically
                                                                      0
                                                                Scorecard     simple score
                                                                            development      variations
                                                                                        is predicated   on (as
                                                                                                           pastshown
                                                                                                               behavioursabove)
                                                                                                                             beingwhich   simplify
                                                                                                                                   predictive  of  the
 xplain the relationships            Age >30                    future45
                                                                       events. Consequently,  scorecard   models utilise historic data as this
                                        implementation
                                     Time                   process. -30
                                           at Address < 6mths          However, they can be more complex, with the increment calculated
 etween the individual                                          information provides quantifiable relationships between the application details
                                     Time  at Address
                                        based         6-12mths
                                                 on the  underlying   -10
                                                                      characteristic.
 haracteristics. The                 Time at Address 6-12mths
                                                                and the
                                                                      0
                                                                         outcome being modelled. This assumption was true when credit
 utcome of this analysis             etc                        scorecards were first developed 45 years ago and it remains true today from both
s a model which utilizes                                       a commercial and consumer perspective.
 ifferent score variations         Scorecard development is predicated on past behaviours being predictive of future events.
 ased on the applicant’s           Consequently, scorecard An analysis
                                                                  models of adverse    credit experiences
                                                                             utilise historic             conducted
                                                                                                data as this          by Dunprovides
                                                                                                             information     & Bradstreet
                                                                                                                                        quantifiable
                                                           demonstrates the correlation between past events and future outcomes. The
 etails.                           relationshipsSample
                                                  betweenScorecard
                                                              the application    details   and  the outcome  being   modelled.   This   assumption
                                                           study reveals that a company is eight times more likely to fail if one of its
                                   was   truevariations
                                              when credit  directors has
                                                             scorecards   a  court
                                                                            were   action
                                                                                    first developed 45 years ago and it remainstotrue
                                                                                            against them and eleven  times more  likely   fail iftoday
 core increments are typically simple score             (asthere
                                                             shown
                                                                 is a above)    which
                                                                      court action   against the company. In addition, the risk of a business
implify the implementation process.from   both athey
                                     However,    commercial     andfailing
                                                      can venture
                                                           be more    consumer
                                                                      complex,
                                                                            doubles  perspective.
                                                                                   withfor companies with a director who has been on the board
                                                           of a previously failed company5.
he increment calculated based on the underlying characteristic.

 corecard development is predicated Anonanalysis  of adverse
                                          past behaviours        credit
                                                              being       experiences
                                                                      predictive     of      conducted by Dun & Bradstreet demonstrates the
                                    correlation
uture events. Consequently, scorecard    models between       past data
                                                 utilise historic
                                                            5
                                                                     eventsas and
                                                                               this future outcomes. The study reveals that a company is
nformation provides quantifiable relationships                Dun & Bradstreet Australia www.dnb.com.au
                                    eight times more likely to fail if onedetails
                                               between     the  application       of its directors has a court action against them and eleven
 nd the outcome being modelled. This assumption was true when credit
 corecards were first developed 45 times   moreand
                                    years ago    likely  to fail iftrue
                                                     it remains     there   is a from
                                                                        today     courtboth
                                                                                          action against the company. In addition, the risk of a
                                                                                                                                             6
                                    business venture failing doubles for companies with a director who has been on the board of
  commercial and consumer perspective.
                                     a previously failed company5.
  n analysis of adverse credit experiences conducted by Dun & Bradstreet
 emonstrates the correlation between past events and future outcomes. The
tudy reveals that a company is eight times more likely to fail if one of its
 irectors has a court action against them and eleven times more likely to fail if
here is a court action against the company. In addition, the risk of a business
enture failing doubles for companies    with a director who has been on the board
 f a previously failed company5.
                                     5
                                       Dun & Bradstreet Australia www.dnb.com.au

                   decisionintellect.com.au                                                                                                   Page 6
Thesame
                 The    same principles
                               principles apply
                                              applyto to
                                                       consumers.
                                                          consumers. Research   revealsreveals
                                                                          Research        that a person
                                                                                                   that awith   a previous
                                                                                                            person    with a
                   credit default
                 previous         is adefault
                             credit    significantly
                                                is ahigher   risk of anhigher
                                                      significantly    adverserisk
                                                                                 event
                                                                                     ofthan   a consumer
                                                                                          an adverse        with no
                                                                                                          event     default
                                                                                                                  than   a
                 consumer      with no default
                   record. Furthermore,    the dollarrecord.
                                                       value ofFurthermore,
                                                                a default has no the  dollarcorrelation
                                                                                  significant  value of to a the
                                                                                                             default    has
                                                                                                                 likelihood
                 noofsignificant    correlation     to  the likelihood    of reoccurrence,       with   a consumer
                      reoccurrence, with a consumer who defaults on a debt of less than $500 just as likely             who
                 defaults
                   to repeaton   a behaviour
                              this  debt of lessas athan    $500who
                                                      consumer     justdefaults
                                                                        as likelyonto   repeat
                                                                                     a more       this behaviour
                                                                                               significant            as a
                                                                                                           sum of money.
                 consumer      who    defaults    on   a  more   significant   sum   of   money.     Consequently,
                   Consequently, low value defaults are highly predictive of large value bank 6defaults6.                low
                 value defaults are highly predictive of large value bank defaults .
                     The accuracy of a scorecard is dependent on the quality of the data that is utilised and the
                 The accuracy of a scorecard is dependent on the quality of the data that is7
                   matching and weighting of the data elements to an appropriate forecasting horizon . The
                 utilised  and the matching and weighting of the data elements to an appropriate
                   value  of
                 forecasting application
                                 horizoninformation
                                          7
                                           . The valuedecreases  substantially
                                                          of application        overtime and
                                                                             information      in most instances
                                                                                            decreases              there
                                                                                                          substantially
                   is a lag between
                 overtime              a credit
                              and in most        event andthere
                                              instances     the credit
                                                                  is a assessment
                                                                        lag between   process.  Therefore
                                                                                         a credit event andit is critical
                                                                                                                 the
                   that the
                 credit      information process.
                         assessment       utilised in the assessment
                                                      Therefore    it is process  is timely
                                                                         critical that  the enough  to predict
                                                                                            information           future
                                                                                                            utilised   in
                   behaviours
                 the  assessmentand outcomes.
                                       process is timely enough to predict future behaviours and
                 outcomes.

                              HIGH
                                                                                                                            Application Data
                                                                              Continual monitoring through a Behavioural    Behavioural Model
                                                                              Model maintains value of data.
                           Relative Worth Of Data

                                                    Over time, Application
                                                    data becomes worthless.

                                     LOW

                                                                          The value of information declines overtime
                                                                                                 Source: Dun & Bradstreet

                 6
                  Defaults
                   6
                     Defaultsresearch
                               research –– Dun
                                           Dun && Bradstreet
                                                  Bradstreet  Australia
                                                             Australia    www.dnb.com.au
                                                                       www.dnb.com.au
                 7
                  Credit
                   7     risk
                     Credit riskassessment    revisited:Methodological
                                 assessment revisited:   Methodological    issues
                                                                        issues and and  practical
                                                                                   practical       implications.
                                                                                             implications.       European
                                                                                                           European        Committee
                                                                                                                    Committee of Central of
                   Balance
                 Central    Sheet Data
                         Balance    SheetOffices
                                            Data2007
                                                  Offices 2007

decisionintellect.com.au                                                                                                                       Page 7
The lifespan of a scorecard is dependent on a number of factors, including:
                    • the outcome being modelled;
                    • major changes within the business; and
                    • external impacts such as we are currently experiencing.
                          The lifespan of a scorecard is dependent on a number of factors, including:
                              ! the outcome being modelled;
                  A typical credit scorecard will have a three year operational lifespan – by this time the sample
                              ! major changes within the business; and
                  data used to! build the impacts
                                 external model will beasfive
                                                  such     weyears  old. experiencing.
                                                              are currently

                           A typical credit scorecard will have a three year operational lifespan – by this
                           time the sample data used to build the model will be five years old.

                                                Scorecard Effective Lifetime
                                                               (sample only)
                                               Sample Period    Outcome Period       Production Life

                                  Data          Outcome             Live            Now
                                 Modelled        Period
                                  2006           2007              2008             2009

                                                     3 years
                             0    3   6   9   12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57

                                                      Months from start of available data

                                                                                            Source: Decision Intellect

                          Putting this into perspective, credit decisions being made today may be based on
                          information that could be up to five years old depending on when the model was
                  Putting this into perspective,
                          implemented.    This datacredit
                                                    woulddecisions
                                                           have beenbeing   made
                                                                       obtained   today
                                                                                when    may
                                                                                     credit   beatbased
                                                                                            was           on information
                                                                                                   its peak
                          and economic conditions were favourable.
                  that could be up to five years old depending on when the model was implemented. This
                  data would  have been
                          Scorecards are aobtained     when incredit
                                           critical element           was
                                                                the risk   at its peak
                                                                         assessment    and economic
                                                                                     process. However, conditions
                                                                                                        they      were
                          require
                  favourable.     ongoing evaluation   and maintenance    to ensure they maintain their
                         effectiveness. This includes adjustments to respond to decreases in the value of
                         information over time, internal company changes and external shifts such as
                         changes
                  Scorecards  are ina the  economic
                                       critical     cycle.in the risk assessment process. However, they require
                                                element
                  ongoing evaluation and maintenance to ensure they maintain their effectiveness. This includes
                  adjustments to respond to decreases in the value of information over time, internal company
                  changes and external shifts such as changes in the economic cycle.

                                                                                                                    8

decisionintellect.com.au                                                                                                 Page 8
The impact of the global
                     credit crisis on scoring
                     models
                     The use of scorecards for credit assessment is still a relatively new phenomenon in Australia.
                     Therefore, the economic shock that is currently being experienced is significantly more acute
                     than the previous two experiences – the 1987 tech crash and September 11 – that have
                     occurred since scorecards became entrenched in the credit assessment process.

                     Consequently, Australian firms are now facing a situation where they have no historic
                     precedent to refer to for direction and where the rules that have been in place for some time
                     no longer apply.

                       “In a severe economic contraction, whatever rules you were using before, are
                       no longer the same.”

                       Jerry Flum, CreditRiskMonitor

                     Understanding how credit scoring models will be impacted by current conditions requires a
                     complete appreciation of the factors that will influence behaviours and capacity to repay. From
                     a commercial perspective, the key trends that will impact credit capacity and performance
                     are:

                     Business failures: The number of companies entering external administration rose by twenty
                     per cent in 20088.

                     Corporate risk: The number of firms at risk of experiencing financial distress or failure during
                     2009 has increased by twelve per cent on 2008 figures9.

                     Cashflow and re-financing issues: Many companies have been relying on cheap funding
                     from banks and they are now faced with an environment in which it is difficult to access cheap
                     credit or in some instances, to access credit at all.

                     Slowing payment terms: Businesses are currently averaging 56.5 days to settle accounts,
                     the highest level since 200110.

                     The impact of these trends began to reveal themselves during 2008. An examination of
                     insolvency appointments from 2001-2008 reveals a sharp increase in the later half of 2008,
                     with appointments rising to the highest level in more than eight years. Given insolvencies tend
                     to lag the economic cycle this trend is expected to continue at least throughout 2009.

                 8
                   Business failures – Dun & Bradstreet Australia www.dnb.com.au
                 9
                   Corporate risk – Dun & Bradstreet Australia www.dnb.com.au
                 10
                    Trade payments analysis – Dun & Bradstreet Australia www.dnb.com.au
decisionintellect.com.au                                                                                      Page 9
The impact of these trends began to reveal themselves during 2008. An
                   From a consumer perspective the key influences on credit performance are:
                     examination of insolvency appointments from 2001-2008 reveals a sharp
                     increase   in the
                       The impact        latertrends
                                    of these      half ofbegan
                                                           2008,towith   appointments
                                                                    reveal                 rising2008.
                                                                           themselves during       to the
                                                                                                        Anhighest level in
                   Unemployment:
                     more   than eight
                       examination      The
                                          years.number
                                     of insolvency        of unemployed
                                                      Given             fromAustralians
                                                              insolvencies
                                                        appointments          tend to lag
                                                                             2001-2008    has  steadily
                                                                                             the
                                                                                           reveals       decreased
                                                                                                  economic
                                                                                                    a sharp   cycle over
                                                                                                                    this the past
                   few increase
                         years
                     trend       in the later
                               however
                            is expected        ishalf
                                           it to      of 2008,to
                                                  expected
                                                  continue    atwith
                                                                 riseappointments
                                                                 leasttothroughout  rising
                                                                         5.6% in 2009   11 to the highest level in
                                                                                          . Some commentators are predicting
                                                                                      2009.
                         more than eight years. Given insolvencies tend to lag the economic cycle this
                   it totrend
                          be inisdouble digits
                                  expected     by 2010.at least throughout 2009.
                                           to continue

                                                         The impact of these trends began to reveal themselves during 2008.
                                                         examination of insolvency appointments from 2001-2008 reveals a sh
                                                         increase in the later half of 2008, with appointments rising to the high
                                                         more than eight years. Given insolvencies tend to lag the economic c
                                                         trend is expected to continue at least throughout 2009.

                                                                                                                                 Source: Source:
                                                                                                                                         ASIC    ASIC

                         From a consumer perspective the key influences on credit performance are:
                        From a consumer perspective the key influences on credit performance are:
                                !   Unemployment: The number of unemployed Australians has steadily
                   Personal  insolvencies:
                        ! Unemployment:
                            decreased         The
                                        over the   number
                                                 The
                                                 past      of personal
                                                      number
                                                      few yearsofhowever  insolvencies
                                                                  unemployed                increased
                                                                                      Australians
                                                                            it is expected   to rise has by twelve per cent
                                                                                                          steadily
                                                                                                     to 5.6%
                                   11
                           decreased
                            in 2009   .  over
                                        Some
                   year-on-year (December      the past few
                                               commentators 12years
                                              quarter 2008) .        however
                                                              predicting it  to be it
                                                                                    inis expected
                                                                                        double digitsto
                                                                                                      byrise to 5.6%
                                    11
                                     2010.
                                    in 2009 . Some commentators predicting it to be in double digits by
                                    2010.

                                                         Australian Unemployment Rate
                                                                           Source: D&B
                                                7.0%       Australian Unemployment Rate
                                                                                                                 6.0%
                                                6.0%
                                                                               Source: D&B       5.7%

                                                 7.0%    4.8%
                                                5.0%                4.4%         4.3%                                     6.0%
                                                 6.0%                                                     5.7%
                                                4.0%
                                                         From
                                                          4.8% a consumer perspective the key influences on credit performan
                                                3.0%
                                                 5.0%                  4.4%              4.3%
                                                2.0%
                                                 4.0%           !   Unemployment: The number of unemployed Australians has
                                                1.0%
                                                 3.0%
                                                                    decreased over the past few years however it is expected to ri
                                                0.0%
                                                 2.0%
                                                                    in 200911. Some commentators predicting it to be in double di
                                                         2006
                                                                    2010.
                                                                     2007  2008 (est) 2009 (est) 2010 (est)
                                                 1.0%

                                                 0.0%
                                                           2006        2007         2008 (est)          2009 (est)      2010 (est)
                         11
                              Economic & Risk Outlook – Dun & Bradstreet
                                                                                                   Australian Unemployment Rate
                                                                                                                            Source: D&B
                                                                                    7.0%
                        11                                                                                                                  10            6.0%
                             Economic & Risk Outlook – Dun & Bradstreet             6.0%                                                     5.7%
                                                                                                   4.8%
                                                                                    5.0%                             4.4%            4.3%
                                                                                    4.0%
                   11
                        Economic & Risk Outlook – Dun & Bradstreet                                                                                   10
                                                                               3.0%
                   12
                        Provisional personal insolvency statistics – Insolvency & Trustee Service Australia www.itsa.gov.au
                                                                                    2.0%

decisionintellect.com.au                                                            1.0%                                                            Page 10
                                                                                    0.0%
Source: Dun and Bradstreet

                               !   Personal insolvencies: The number of personal insolvencies increased
                                   by twelve per cent year-on-year (December quarter 2008)12.

                   Declining    household
                         ! Declining         wealth: Over
                                         household           the Over
                                                       wealth:    coursetheofcourse
                                                                              2008 household     wealth decreased
                                                                                      of 2008 household   wealth
                             decreased    significantly. In many   instances   this related to
                   significantly. In many instances this related to depreciating asset values. depreciating
                                   asset values.
                   Savings   and debt:and
                        ! Savings       Australians   have low saving
                                              debt: Australians    haverates   and high
                                                                          low saving     debtand
                                                                                      rates   to income   ratios
                                                                                                   high debt   to . Our
                                                                                                                 13

                   debt to income
                             incomeratio  is 13one
                                     ratios        of the
                                                . Our  debthighest in theratio
                                                             to income     world  and of
                                                                               is one while
                                                                                         thethere
                                                                                             highesthasinbeen   a sharp
                                                                                                          the world
                             and  while there    has been   a  sharp switch   from  spending   to  saving  in the
                   switch from spending to saving in the last couple of months our level of personal debt is still
                             last couple of months our level of personal debt is still extremely high.
                   extremely high. This will become a significant issue as unemployment increases.
                                   This will become a significant issue as unemployment increases.

                                                                                          Household debt-to-income ratio
                                      Household saving ratio                           Source: Reserve Bank of Australia and Treasury.
                        Source: Australian Bureau of Statistics (ABS), Australian
                        National Accounts, cat. no. 5206.0, Canberra, 2008.

                        The impact of these trends often takes many months to show through due to the
                        lag between the onset of financial hardship and a negative credit event occurring.
                        However, the effects are already beginning to impact debt performance.
                   The impact of these trends often takes many months to show through due to the lag between
                   the onset of financial hardship and a negative credit event occurring. However, the effects are
                   already beginning to impact debt performance.

                        12
                             Provisional personal insolvency statistics – Insolvency & Trustee Service Australia www.itsa.gov.au
                        13
                             Household saving in Australia - www.treasury.gov.au

                                                                                                                                         11

                   13
                        Household saving in Australia - www.treasury.gov.au

decisionintellect.com.au                                                                                                          Page 11
Handling the crisis
                 – differences in the
                 preparedness of various
                 sectors
                 The rules and regulations that credit providers are required to adhere to regarding credit
                 assessment processes and provisioning vary depending on the sector in which they operate.
                 As a result, some sectors may be better placed to manage their portfolios and new customer
                 acquisition processes in this new and continually shifting environment.

                 The Basel II Capital Framework (Framework), which came into effect in Australia on 1 January
                 200814, is intended to encourage organisations to identify the risks they may face and to
                 develop or improve their ability to manage those risks. It also aims to improve flexibility, allowing
                 systems to evolve with advances in markets and risk management practices15.

                 Under the Framework banking and finance organisations are required to have analytical and
                 quantifiable process in place for assessing customers. In addition, they are required to stress
                 test their scoring models. This is an important process for assessing how a portfolio will respond
                 to changes in the market such as interest rate movements or rising unemployment. This has
                 led to significant improvements to the risk assessment practices of many financial institutions.
                 Consequently Basel II has provided a much clearer picture of the quality of existing portfolios,
                 revealing that exposure levels are often significantly higher than previously realised.

                 Ideally, this situation should encourage financial institutions to improve their systems and
                 processes and a recent survey of global banking executives indicates that process improvements
                 will become a key focus. The survey, conducted by KPMG, reveals that almost eight out of ten
                 respondents are seeking to improve the way risk is reported and measured. Their emphasis is
                 expected to focus on stress testing and Basel II credit models16.

                 However as regulators looks for way to avoid another crisis, Basel II is likely to come under
                 scrutiny. The current process of self-regulation and of trusting internal models without a need
                 for external checks may disappear. This may result in either of two outcomes:

                 1.   Independent reviews and models may be mandated and used as a benchmark to ensure
                      that internal ratings are within acceptable limits.

                 2.   Minimum requirements may be set, including the mandated use of external models.

                 14
                    Response to submissions: Implementation of the Basel II Capital Framework www.apra.gov.au
                 15
                    Basel II: Revised international capital framework – Bank of International Settlements www.bis.org
                 16
                    Never again? Risk management in banking beyond the credit crisis. KPMG International, February 2009

decisionintellect.com.au                                                                                                  Page 12
Australia’s banks need to prepare themselves for these possibilities. However, regardless
                 of the potential regulatory changes that may be on the horizon they should ensure that
                 they implement the best possible processes so they have the confidence to lend without
                 detrimentally impacting their risk profile and bad debt provisioning.

                 For credit providers outside of the banking and finance industry there are lessons that can be
                 learnt from Basel II. Telecommunications and utilities providers could gain significant benefits
                 from implementing infrastructure which incorporates stress testing and which has the capacity
                 to evolve with advances in markets and risk management practices. By examining the impact
                 of various economic factors on a portfolio, these processes enable an organisation to gain a
                 thorough understanding of the risk in their existing portfolio as well as the risk associated with
                 new customer acquisitions.

                 Importantly, credit providers need to be diligent with their testing, ensuring that it is conducted
                 at regular intervals. Despite its many benefits, in periods of economic prosperity such as
                 Australia experienced prior to the onset of the global credit crisis, stress testing is often
                 deemed an unnecessary practice and given little priority.

                 Complacency in risk assessment is what caused the financial meltdown. The lesson to be
                 learnt here is that stress testing is important across all sectors (not just for those organisations
                 that are required to be Basel II compliant). Credit providers need to put the disciplines in
                 place that enable effective assessment prior to the extension of credit as well as ongoing
                 assessment.

decisionintellect.com.au                                                                                       Page 13
Aligning scoring models
                 with risk appetite

                 Many of the scorecards that are currently being used in Australia were built on data which
                 was sourced during a period of economic strength – unemployment was at record lows,
                 share markets and asset values were rising and credit was easily accessible. The situation
                 is markedly different now. Unemployment is rising, market conditions are deteriorating and
                 consumer spending is declining – all of these trends will feed directly into a higher level of
                 corporate delinquencies and consumer defaults.

                 Given the extent of the changes that have taken place over the past twelve months, and
                 the expectation that conditions will continue to deteriorate, the obvious question is will credit
                 scoring models continue to work in the current climate?

                 From the perspective of ranking potential customers according to the likelihood of default yes,
                 the models will continue to work, however the odds associated with the scores will change. This
                 was confirmed by some recent analysis undertaken by Decision Intellect. The shifts should not
                 be too drastic but credit providers will need to adjust their cut-off scores in order to prevent
                 substantial increases in bad debt.

                   “What does it all mean for credit and collections professionals? It’s a triple whammy. It
                   might even be a quadruple or quintuple whammy: there might be more of it. At the very
                   least, you have consumer confidence plummeting. You have recession in a number of
                   countries, which is going to cause bad debt.”

                   Chris Hobson, Chief Marketing Officer, Cortera

                 A standard scorecard will assign a cut-off level which allows good customers to compensate
                 for the bad. For example, if a good customer generates $500 profit and a bad customer costs
                 an average of $4,000 then a cut-off score where the odds are 8:1 will result in a marginal profit
                 for these customers. Any customers below this cut-off point would erode profits while any
                 above that level add value for the business.

                 If the odds change the score must be re-calculated as the relationship between the odds and
                 the score will no longer hold true. This is the underlying issue impacting the effectiveness of
                 credit scoring models in the current climate.

                 The issue for most organisations is that the current crisis is a new and unprecedented event
                 with no prior precedent that can be used as a proxy for adjusting scoring models.

decisionintellect.com.au                                                                                   Page 14
120
                                                                                              Development Score Distribution
                                                                                                            (sample data only)
                                                                                                                                                                                        105.0

                                           100
                                                                       120
                                                                                                               Development Score Distribution
                                                        Declined Applications                                            (sample data only)
                           Good Bad Odds

                                                                                                                                                                                        105.0
                                                        based on scorecard                                                                                                     78.0
                                            80               100
                                                        development  population.
                                                                                       Declined Applications
                                                       Good Bad Odds

                                                                                       based on scorecard                                                                       78.0
                                            60                          80
                                                                                       development population.

                                                                        60                                                                                         38.5
                                            40
                                                                                                                                                     27.6               38.5
                                                                        40                                                          22.8
                                                                                                                      17.8                                  27.6
                                            20                                                                                                22.8
                                                                                                        11.2
                                                                        206.6
                                                                                         8.0                                     17.8
                                                 2.4                                                                  11.2
                                                                                               6.6         8.0
                                             0                                   2.4
                                                                                                     Scorecard Risk Band
                                                                         0
                                                 1                           2            3          (from4development 5sample)
                                                                                                                  Scorecard          6
                                                                                                                               Risk Band              7             8           9        10
                                                                                 1             2            3     (from4development5sample)    6             7            8         9    10

                                                                                                                                                                     Source:
                                                                                                                                                                      Source: Decision   Intellect
                                                                                                                                                                              Decision Intellect

                         If the odds
             If the odds change      change
                                   the scorethemust
                                                scorebe
                                                      must   be re-calculated
                                                          re-calculated    as as
                                                                               thetherelationship
                                                                                       relationship between
                                                                                                    between
                         the odds and the score will no longer hold true. This is the underlying issue
             the odds and    the score  will no longer   hold   true. This  is the   underlying    issue
                         impacting the effectiveness of credit scoring models in the current climate.
             impacting the effectiveness of credit scoring models in the current climate.
                 That said,
                          Theintervention  is critical
                               issue for most          if significant
                                              organisations     is that bad  debt issues
                                                                        the current         are
                                                                                    crisis is   to be
                                                                                              a new     avoided. Failure to
                                                                                                      and
                 adjust and
             The issue   for  implement
                          unprecedented
                             most         strategies
                                           event with to
                                    organisations      nopre-empt
                                                       is   prior the
                                                           that        the potential
                                                                   precedent
                                                                       currentthat canimpacts
                                                                                 crisis be
                                                                                         is usedofasthe
                                                                                            a new       current
                                                                                                      a proxy
                                                                                                      and     forcrisis could
             unprecedentedadjusting scoring
                                event   with models.
                                             no   prior   precedent      that  can  be   used   as    a proxy   for
                 result in significant, potentially unrecoverable, debt issues.
             adjusting scoring  models.
                         That said, intervention is critical if significant bad debt issues are to be avoided.
                          Failure
                   Therefore      to adjust
                             the best       and implement
                                      approach            strategies
                                                 is to adopt economicto pre-empt
                                                                        principlesthe
                                                                                   andpotential
                                                                                        create impacts  of can be
                                                                                                proxy’s that
             That said, intervention     is critical
                          the current crisis         if significant
                                             could result             badpotentially
                                                          in significant,  debt issues     are to bedebt
                                                                                     unrecoverable,   avoided.
                  used to issues.
                           estimate the new score verse odds relationship. There are many different variables
             Failure to adjust and implement strategies to pre-empt the potential impacts of
                  that cancrisis
             the current    be used  as a
                                 could      proxy.in The
                                          result          followingpotentially
                                                     significant,    work looks unrecoverable,
                                                                                  at modelling the differences
                                                                                                     debt      in the
                          Therefore  the  best approach   is to adopt economic   principles and create proxy’s
                  bad rates that are evident between the time of development and a recent sample (or latest
             issues.      that can be used to estimate the new score verse odds relationship. There are
                  monitoring
                          many sample).
                                  different variables that can be used as a proxy. The following work looks
             Therefore the     best approach
                          at modelling             is to adopt
                                         the differences            economic
                                                            in the bad            principles
                                                                         rates that are evidentand    create
                                                                                                 between    theproxy’s
                                                                                                                 time of
             that can  be development
                           used    to     and a recent
                                       estimate    the    sample
                                                        new         (or latest
                                                                score    verse monitoring
                                                                                 odds     sample).
                                                                                        relationship.      There     are
                  Other variables can be used as a proxy for the potential variation in the score odds relationship
             manyhowever
                    different   variables
                            a high    degreethat
                                              of becan be over
                                                  visibility  usedthe asselected
                                                                           a proxy.variable
                                                                                       The following
                                                                                              is critical  workis alooks
                                                                                                           as score  process of
                          Other   variables  can     used as a proxy      for the potential variation  in the
             at modellingodds
                            the relationship
                                  differences     in the   bad    rates   that  are  evident   between
                                               however a high degree of visibility over the selected variablethe   timeis of
                  continuous review and adjustment.
             development     and    a  recent   sample     (or   latest   monitoring
                          critical as is a process of continuous review and adjustment. sample).

             Other variables can be used as a proxy for the potential variation in the score
             odds relationship however a high degree of visibility over the selected variable is
             critical as is a process of continuous review and adjustment.

                                                                                                                                                                                                15

decisionintellect.com.au                                                                                                                                                                         Page 15
One potential proxy for understanding the difference between the development and recent
                   data would be to model the difference in experienced bad rate compared to the development
                     One potential proxy for understanding the difference between the development
                   bad rate.
                     and     Thedata
                          recent  example
                                       wouldbelow
                                             be to highlights
                                                   model thewhere    the experienced
                                                              difference             rate
                                                                         in experienced   shows
                                                                                        bad rate the increase in bad
                   accounts
                     comparedbeing  experienced.
                                to the development bad rate. The example below highlights where the
                     experienced rate shows the increase in bad accounts being experienced.

                      One potential proxy for understanding
                                                     Monthly the
                                                               Baddifference
                                                                      Rate between the development
                      and recent data would be to model  thev Development
                                                    Current   difference in experienced bad rate
                      compared  to the development bad rate. The example below highlights where the
                              18%
                      experienced rate shows the increase in bad accounts being experienced.
                               16%

                               14%
                                 12%
                                                                    Monthly Bad Rate
                                                                    Current v Development
                              te
                               a 10%
                               R 18%
                               d 8%
                               a
                               B 16%
                                  6%
                                 14%
                                  4%
                                 12%
                               e
                               t 2%
                               a 10%
                               R
                               d 0%
                               a 8%
                               B        1      2         3          4          5     6       7       8       9           10          11
                                  6%
                                                                             Months since application
                                 4%
                                 2%    Development                 Current            Development Trend                  Current Trend

                                 0%                                                                           Source: Decision Intellect
                                        1      2         3          4          5     6       7       8        9          10          11
                     The analysis, which will likely reveal Monthsa higher
                                                                       sincerate   of customers turning bad, can
                                                                             application
                     then be used to forecast an expected “bad rate”. Overlaying this forecast onto the
                   The  analysis,
                     initial score which  will likely
                                     Development
                                   distribution        reveal
                                                         Currenta
                                                 will provide     higher
                                                                the       rate  of customers
                                                                       Development
                                                                    proxy   for the    new scoreturning
                                                                                      Trend     Current   bad, can then be used
                                                                                                 odds Trend
                                                                                                        relationship.
                   to forecast an120
                                    expected “bad rate”. Overlaying this forecast onto    theDecision
                                                                                      Source:  initial score    distribution will
                                                                                                           Intellect
                   provide the proxy for the new score odds
                                                        Forecast    relationship.
                                                                 Score   Distribution                105.0
                      The analysis,
                                 100 which will likely reveal a higher rate of customers turning bad, can
                                                              (sample data only)
                      then be used to forecast an expected “bad rate”. Overlaying this forecast onto the
                                                                                            78.0
                      initial score
                                s 80
                                    distribution will provide the proxy for the new score odds    relationship.
                               d
                               d
                               O 120
                               d 60
                               a                                  Forecast Score Distribution                                             105.0
                               B
                               d 100                                         (sample data only)                   38.5
                               o 40
                               o
                               G                                                                     27.6
                                                                                            22.8                              78.0
                                  80                                               17.8
                               s 20
                               d                             8.0
                                                                        11.2
                               d                   6.6
                               O         2.4
                               d 60
                               a 0
                               B
                               d         1         2         3           4       5       6               7         8
                                                                                                                  38.5         9          10
                               o 40                                      Scorecard Risk Band
                               o
                               G                                      (from development sample)      27.6
                                                                                         22.8
                                                               Development 17.8                          Experienced
                                  20                                 11.2
                                                   6.6       8.0
                                         2.4
                                                                                                              Source: Decision Intellect
                                   0
                                         1          2         3          4       5       6               7         8           9           10
                                                                         Scorecard Risk Band
                                                                         (from development sample)
                                                                 Development                             Experienced

                                                                                                                                                  16
                                                                                                               Source: Decision Intellect

                                                                                                                                                  16

decisionintellect.com.au                                                                                                                               Page 16
In developing this proxy, careful consideration must be given to the information being utilised.
                 In developing   this proxy,
                    Current experienced      careful
                                          data may notconsideration
                                                          be reflectivemust
                                                                         of thebeexpected
                                                                                   given tooutcomes
                                                                                           the information
                                                                                                      that will occur in
                 being utilised. Current experienced data may not be reflective of the expected
                    the months ahead. To account for the expected changes the scorecard equations can include
                 outcomes that will occur in the months ahead. To account for the expected
                    a forecast
                 changes   the value. The result
                                scorecard        will becan
                                           equations     a curve  that areflects
                                                            include              forecast
                                                                          forecast  value.additional
                                                                                           The resultdefaults.
                                                                                                       will be
                 a curve that reflects forecast additional defaults.

                                      In developing this proxy, careful consideration must be given to the information
                                      being                        Forecast Score
                                           120utilised. Current experienced    dataDistribution
                                                                                      may not be reflective of the expected
                                                                         (sample data only)                       105.0
                                      outcomes that will occur in the months ahead. To account for the expected
                                           100
                                      changes the scorecard equations can include a forecast value. The result will be
                                      a curve
                                         s
                                            80
                                                that reflects forecast additional defaults.                78.0
                                           d
                                           d
                                           O
                                           d
                                           a 60
                                           B                          120                                                  Forecast Score Distribution
                                           d                                                                                            (sample data only)                                                              105.0
                                           o                                                                                                                                             38.5
                                           o 40
                                           G                          100
                                                                                                                                                                       27.6
                                                                                                                                                        22.8
                                                                     s                                                                  17.8                                                             78.0
                                                          20         d 80                                              11.2
                                                                     d            6.6                  8.0
                                                                     O
                                                                     2.4
                                                                     d                                       5.6               7.84
                                                            0        a 60
                                                                     B
                                                                     d
                                                                     o1            2                   3            4        5      6                                   7                 8   38.5       9              10
                                                                     o 40
                                                                     G                                            Scorecard Risk Band                                           27.6
                                                                                                                  (from development sample)                     22.8
                                                                                                                                                   17.8
                                                                        20 Development                                  Experienced
                                                                                                                                     11.2
                                                                                                                                                              Forecast (10% increase in bad rate)
                                                                                                   6.6                 8.0
                                                                                   2.4
                                                                                                                             5.6            7.84
                                                                            0
                                                                                    1                  2               3          4        5      6                              7        Source:
                                                                                                                                                                                            8     9Decision
                                                                                                                                                                                                       10   Intellect
                                                                                                                                Scorecard Risk Band
                                                                                                                                (from development sample)
                                                                                                 Development                          Experienced                     Forecast (10% increase in bad rate)

                 The new score distribution can be used to estimate the expected outcomes that
                 would                                                            Source: Decision Intellect
                   The be
                        newexperienced    at each
                             score distribution   score,
                                                can      thereby
                                                    be used       allowing
                                                             to estimate thethe score
                                                                              expectedcut-off  to be
                                                                                        outcomes    that would be
                 adjusted to reflect the new odds.
                   experienced at each score, thereby allowing the score cut-off to be adjusted to reflect the
                        The new score distribution can be used to estimate the expected outcomes that
                   new odds.
                        would be experiencedScore
                                                at each  score, thereby allowing the score cut-off to be
                                                    Distribution Sample
                        adjusted to reflect the new(sample
                                                    odds.data only)
                                    120
                                                                                                                                                                                                                             105.0

                                    100                                                                          Score Distribution Sample
                                                                                                                             (sample data only)
                    Good Bad Odds

                                                              120
                                                                Declined Applications                                        Additional declines                                                             78.0
                                    80                                                                                       to keep constant                                                                                   105.0
                                                                based on scorecard
                                                              100
                                                                development population.                                      bad debt level
                                          Good Bad Odds

                                    60                                                                                                     Additional declines
                                                                                Declined Applications                                                                                                          78.0
                                                                80                                                                         to keep constant
                                                                                based on scorecard
                                                                                development population.                                    bad debt level                                 38.5
                                    40
                                                                60                                                                                                     27.6
                                                                                                                                                       22.8
                                                                                                                                    17.8
                                    20                                                                                                                                                          38.5
                                                                40                                           11.2
                                                                     6.6                8.0
                                                                                                                    7.84                                                        27.6
                                                      2.4                                        5.6                                                           22.8
                                                                                                                                              17.8
                                      0                         20
                                                                                                                             11.2
                                                          1             2                    3             8.0    4
                                                                                    6.6
                                                                                                                 5.6                7.845                 6                 7                   8               9                 10
                                                                      2.4
                                                                 0                                         Scorecard Risk Band
                                                                                                           (from development sample)
                                                                        1                2                   3                  4                  5              6                  7               8              9              10
                                                                                                          Scorecard Risk Band
                                                                                              Development (from                                                       New Distribution
                                                                                                                development sample)

                                                                                                                 Development                                                              Source: Decision Intellect
                                                                                                                                                                              New Distribution

                                                                                                                                                                                                Source: Decision Intellect

                                                                                                                                                                                                                                        17
                                                                                                                                                                                                                                        17

decisionintellect.com.au                                                                                                                                                                                                                     Page 17
Maintaining customer acquisition targets and current bad debt levels in the current climate is a
                   difficult task for three key reasons:

                    1. maintaining current scorecard acceptance levels will result in increased bad debt

                    2. adjusting the cut-off level will likely result in reduced revenue because a smaller number of
                       applicants will be approved

                    3. some existing customers that have historically exhibited positive credit behaviours will now
                       default.

                   Consequently organisations need to make a choice between meeting the customer acquisitions
                   targets that have been set and accepting a significant increase in bad debt or downgrading
                   the acquisition targets and minimising the increase in bad debt write-off. Credit managers also
                   need to consider how to manage their existing portfolio.

                   Some organisations utilise a range of scorecards for varying purposes, often running up to four
                   different models to assess items including:
                    • the likelihood that the bill will never be paid;
                    • fraud; and
                    • churn.

                   All of these models will need to be adjusted to account for changing economic conditions.
                   However, because the outcome period for some of these events is relatively short, changing
                   economic conditions are less likely to impact these scorecards if they are already being
                   monitored effectively.

                   Consider fraud or non-payment for example. The outcome period for both of these actions is
                   usually around three months. Consequently, regular scorecard monitoring will identify whether
                   or not the scorecards are working and adjustments can be made as required.

                   The impact of the current climate on scorecards also needs to be considered from an industry
                   perspective. In the banking industry, consumer defaults are expected to be a significant issue
                   in the second half of the year as individuals who are facing financial difficulties struggle to
                   re-finance their loans. As a result banks may cease lending for a period of time creating a
                   situation which is relatively similar to the current commercial situation where credit is simply
                   not available.

decisionintellect.com.au                                                                                   Page 18
For telecommunications and utilities providers who operate in the mass market space, the
                   challenges are different. A significant drop in acceptance rates will result in a dramatic increase
                   in the unit cost per customer. Therefore credit providers must decide whether the potential
                   increase in bad debt will be more or less detrimental to the business than an increase in the
                   per unit cost of their service.

                   A decision to cease consumer lending for a period of time would have a more significant
                   detrimental impact on telecommunications and utilities providers than it would for the banking
                   sector.

                   Conversely, managing those clients in the existing portfolio that have traditionally been good
                   payers and may now be forced into negative payment behaviours has the potential to be most
                   challenging for the banking sector due to the significant sums of money often associated with
                   their lending.

decisionintellect.com.au                                                                                      Page 19
Effective utilisation of
                 credit scoring models

                 Automation in the credit decisioning process

                 It is evident that credit scoring models will continue to be robust enough to risk rank customers.
                 However, if they are not re-visited to account for the impact of current economic conditions on
                 credit capacity and behaviour, credit providers will experience a significant increase in bad
                 debt.

                 This need to change should not be perceived as a burden, rather it provides an opportunity for
                 organisations to ensure they have a system in place which enables them to effectively assess
                 and manage customers throughout all aspects of the credit lifecycle.

                   “What I have said for 40 years is if you are in the credit business, you are going to have
                   a credit loss at some point in time. What is totally unacceptable is an administrative loss.
                   An administrative loss is a credit loss because somebody fell asleep at the switch; you
                   don’t have proper procedures, and you don’t have proper policies.”

                   Phil Gootee, Global Credit

                 Automation (which includes sophisticated scoring models) is the foundation of a credit lifecycle
                 system and it can provide substantial improvements for lenders in terms of credit quality,
                 reduced cost, speed of application and consistency:

                 Credit quality: On average scorecards provide a 15-20 per cent improvement in bad debt
                 reduction.

                 Quality data: Accurate and up-to-date information is crucial to effective decisions and achieving
                 optimum results requires the use of internal and external data sources. An automated credit
                 decisioning process should bring the desired sources of information together, utilising them in
                 the scoring process.

                 Costs: In the vast majority of cases automated systems link an organisations contact centre
                 and billing system, thus drastically minimising or removing the manual actions required during
                 the application and account set-up process.

decisionintellect.com.au                                                                                   Page 20
Consistency and compliance: Automation of policy and scoring ensures that all credit
                 applicants have undergone the same level of assessment and that all personal and cultural
                 bias is removed from the process.

                 Speed of application: Consumers and businesses now expect on the spot credit approval,
                 particularly when the required service is relatively low value such as a phone or utilities
                 account. The automated decisioning systems that are available in Australia can assess an
                 application (consumer) in 5-10 seconds (and they can process a large number of applications
                 simultaneously), enabling instant approval / decline of credit applications.

                 Flexibility: The ability to adapt systems and processes quickly and efficiently is critical to
                 managing risk, particularly during times of economic change. Achieving this outcome requires
                 continual monitoring and evaluation to ensure that changes in risk can be identified and
                 acted upon before they result in detrimental impacts. In an automated environment the policy
                 and risk assessment rules are contained within the technology, enabling speed and ease of
                 change if and when required. A lender can increase / decrease acceptance rates by simply
                 adjusting the score cut offs – achieving this outcome in a manual environment is much more
                 difficult.

                 The global financial crisis has revealed a clear need for flexibility and the ability to conduct
                 timely system changes in credit assessment processes. Achieving these outcomes can be
                 relatively simple as the capabilities are already available – automation is the enabler of an
                 efficient, effective and flexible credit assessment system.

decisionintellect.com.au                                                                                  Page 21
Looking ahead to
                 comprehensive reporting
                 Australia is one of just three countries in the developed world that operates under what is
                 known as a negative consumer credit reporting model. Under this system a credit report is only
                 allowed to contain identification details, a list of credit applications and negative events such
                 as defaults or bankruptcies. Importantly, credit reports can not currently detail whether a credit
                 application has been approved. Changes to this system were proposed by Dun & Bradstreet
                 in 2004.

                 Following an extensive review of the Privacy Act, including public consultation, the Australian
                 Law Reform Commission has recommended to the Federal Government that Australia move
                 from a negative-only consumer credit reporting model to a more comprehensive system.

                 Should this recommendation be accepted, a consumer credit report would be able to contain
                 four additional pieces of information:
                   1. Type of credit - e.g. mortgage, personal loan, credit card
                   2. Lending institution
                   3. Credit limit
                   4. Date account is closed

                 The availability of the recommended additional data elements will provide many benefits for
                 consumers, SMEs and lenders. Critically, it will assist in enabling the free flow of credit in a
                 responsible manner.

                 One of the significant challenges lenders currently face is being able to make informed
                 assessments on credit capacity with such limited information available.

                 This occurs because approximately seventy-five per cent of Australian consumers have not
                 had a negative credit event and consequently have minimal information included on their file.
                 In some instances this lack of information results in creditworthy individuals being denied
                 access to mainstream credit.

                 A move to a more comprehensive system would enable lenders to better assess the financial
                 capacity of these consumers as they would be able to demonstrate positive credit behaviours.
                 Furthermore, consumers who have been denied credit due to a previous negative event may
                 be able to offset the negative listing by demonstrating positive payment behaviours.

                 From an SME perspective, comprehensive reporting also produces significant benefits. Credit
                 scoring is the preferred method of risk assessment for most large credit providers and more
                 data is critical to effective scores. By utilising scoring models for SME assessment lenders are
                 able to blend business owner data with small business information, enabling a clearer picture
                 of overall credit capacity. It is for this reason that comprehensive reporting encourages major
                 lenders into the SME credit market, thus improving access and price for small business17.
                 17
                      Roadmap to Reform, Dr Michael Turner, Political & Economic Research Council 2008

decisionintellect.com.au                                                                                    Page 22
The inclusion of additional data elements in credit scoring models is expected to improve the
                       is forofthis
                 accuracy        thereason   that comprehensive
                                      outcomes,                      reportingdirectly
                                                   with the improvement         encourages    major
                                                                                        related       lenders
                                                                                                 to the        into the
                                                                                                          predictive  nature of
                       SME credit market, thus improving access and price for small business17.
                 the data.
                       The inclusion of additional data elements in credit scoring models is expected to
                       improve the accuracy of the outcomes, with the improvement directly related to
                 An analysis    of a portfolio for which application scorecards (no positive data) and behavioural
                       the predictive nature of the data.
                 scorecard (includes positive data) have been developed reveals that behavioural scorecards
                 are significantly
                       An analysis   more
                                       of a predictive    .
                                                        18 which application scorecards (no positive data) and
                                            portfolio for
                       behavioural scorecard (includes positive data) have been developed reveals that
                       behavioural scorecards are significantly more predictive18.
                 For the application credit scorecard those applicants that score in the bottom two per cent of
                       For distribution
                 the score   the application    credit
                                           (those      scorecard
                                                    that would bethose     applicants
                                                                     considered        that
                                                                                   to be  a score  in thecredit
                                                                                            significant    bottom  twoare 12.2
                                                                                                                risk)
                       per cent of the score distribution (those that would be considered to be a
                 times more    likely to default on their credit obligation than those that fall into the top twenty five
                       significant credit risk) are 12.2 times more likely to default on their credit
                 per cent
                       obligation categorised
                           (those                  as afallgood
                                     than those that        into credit
                                                                 the toprisk). This
                                                                          twenty    compares
                                                                                 five           to a behavioural
                                                                                      per cent (those    categorisedscorecard
                       as   a good   credit risk). This  compares    to a  behavioural   scorecard
                 where the likelihood of default is 390 times higher for those in the high risk      where   thecategory than
                       likelihood of default is 390 times higher for those in the high risk category than
                 those those
                        in the in
                                good
                                   the credit  risk group.
                                       good credit   risk group.

                                                                 Comparison of the Percentile Odds Distribution between Behavioural and Application Risk
                                                                                                       Scorecards

                                                600.0
                                                                 Behavioural Risk Score Distribution
                                                                 Application Risk Score Distribution
                                                500.0

                                                400.0
                                Good/Bad Odds

                                                300.0

                                                200.0

                                                100.0

                                                  0.0
                                                                                                                                                                             %
                                                                                        0%
                                                                          %

                                                                                                        5%

                                                                                                                   0%

                                                                                                                                5%

                                                                                                                                           5%

                                                                                                                                                      0%

                                                                                                                                                                 5%
                                                            2%

                                                                                                                                                                            00
                                                                        -5

                                                                                     -1

                                                                                                       -1

                                                                                                                  -2

                                                                                                                             -2

                                                                                                                                          -3

                                                                                                                                                  -5

                                                                                                                                                             -7
                                                        m

                                                                                                                                                                        -1
                                                                     2%
                                                      tto

                                                                                   5%

                                                                                                  %

                                                                                                              %

                                                                                                                            %

                                                                                                                                      %

                                                                                                                                                  %

                                                                                                                                                             %

                                                                                                                                                                        %
                                                    Bo

                                                                                                10

                                                                                                             15

                                                                                                                          20

                                                                                                                                     25

                                                                                                                                                35

                                                                                                                                                           50

                                                                                                                                                                      75

                                                                                                                        Percentile

                           Note: The top 25% for the behavioural risk scorecard was not plotted as the Good/Bad odds for
                           those individuals was 2400 Good accounts for every bad account.

                       The impact of these findings on default rates is significant when applied to a
                       twenty per cent decline rate:
                 The impact of these findings on default rates is significant when applied to a twenty per cent
                 decline17 rate:
                                Roadmap to Reform, Dr Michael Turner, Political & Economic Research Council 2008
                           18
                                Application and behavioural scorecard analysis, Dun & Bradstreet 2008
                       • the application scorecard would avoid 37.1% of all future credit defaulters
                       • the behavioural scorecard performs 2.1 times better, avoiding 78.3% of future credit
                                                                                                     23
                         defaulters.

                 18
                      Application and behavioural scorecard analysis, Dun & Bradstreet 2008

decisionintellect.com.au                                                                                                                                                         Page 23
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