The implications of the global credit crisis for credit scoring models
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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 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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