The implications of the global credit crisis for credit scoring models
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
A D&B | DI Whitepaper
The implications of the
global credit crisis for
credit scoring models
decisionintellect.com.au Page 1
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 2The 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 4Credit 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 5The 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 6Thesame
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 7The 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 8The 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 9The 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 11Handling 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 12Australia’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 13Aligning 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 14120
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 15One 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 16In 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 17Maintaining 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 18For 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 19Effective 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 20Consistency 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 21Looking 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 22The 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 23You can also read
