Credit Scoring Development and Methods

1、Credit ScoringDevelopment and MethodsJames MarinopoulosHead of Retail Decision Model7/1/20221Alan Greenspan:President, Federal Reserve BoardMay 1996“ We should not forget that the basic economic function of these regulated entities (banks) is to take risk. If we minimise risk taking in order to redu

2、ce failure rates to zero, we will, by definition, have eliminated the purpose of the banking system.”Risk FamiliesWe are managing different groups of RiskRetail Decision Models Responsibilities PolicySet Group policy on Decision ModelsApprove Decision Model policy changesMonitor, Validate and Approv

3、eNew Scorecard DevelopmentsExisting Scorecard FunctionalityProposed changes to Decision Models ProcessesNew Decision Models Systems functionality Decision Models Systems functionality changesGovernanceMonitoringUndertake bank validations, reports and presentations for APRARisk MeasurementSet risk be

4、nchmarks for scorecardsRisk grading modelsAdviseWorlds best practice in Decision ModelsRisk related issues surrounding Decision Models7/1/20224RDM Structure and ResponsibilitiesRelationshipDevelopmentsChange RequestsSystemsOngoing ValidationsMonitoringData Analysis7/1/20225Presentation TopicsScoreca

5、rd Modelling Business ObjectivesWorld BanksMonitoringFuture DirectionOverview of scoring7/1/20226What is credit scoring?A statistical means of providing a quantifiable risk factor for a given customer or applicant.Credit scoring is a process whereby information provided is converted into numbers tha

6、t are added together to arrive at a score. (“Scorecard”)The objective is to forecast future performance from past behaviour.Credit scoring developed by Fair & Isaac in early 60sWidespread acceptance in the US in early 80s and UK early 90sFICO scores make 75% of US Mortgage loan decisionsBehavioural

7、scoring accepted as more predictive than application scoringDecision Models are used in many areas of industries:Banking and FinanceInsuranceRetailTelecommunications 7/1/20227Application ScoringApplication scoring is a statistical means of assessing risk at the point of application for credit The ap

8、plication is scored onceApplication scoring is used for:Credit risk determinationLoan amount approvalLimit settingCredit Decision7/1/20228Behavioural ScoringBehavioural scoring is a statistical means of assessing risk for existing customers through internal behavioural dataCustomers/accounts scored

9、repeatedlyBehaviour scoring is used for:AuthorisationsLimit increase/overdraft applicationsRenewals/reviewsCollection strategiesRisk GradingDebit $1344. 12Debit $234. 01Debit $987.56Debit $6543.22Debit $32423.11Total $2556.00Debit $1344. 12Debit $234. 01Debit $987.56Debit $6543.22Debit $32423.11Tota

10、l $2556.00Debit $1344. 12Debit $234. 01Debit $987.56Debit $6543.22Debit $32423.11Total $2556.007/1/20229Sample scorecard characteristicsFinancial Assets LiabilitiesMonthly repaymentTotal Monthly incomeBureauNo. of bureau defaultsAdverse ANZ behaviourApplicationPurpose of loan DepositSecurityCharacte

11、ristics used in scorecards are similar to those used in traditional judgemental lending, e.g.: The difference being that attributes within these characteristics are given formal weights (scores) and added to produce a resulting score CharacterTime at current employmentResidential statusTime at curre

12、nt address7/1/202210Scorecard points (example)Residential status OwnerRenterLWP/Other +25 -30 +10Time in employment (years) 23-45-6 7+ 2 10 15 25Total monthly income 0$500$1000$1500$2000$3000 0 15 25 31 37 43 48Total defaults No Defaults 1 2+ 0 -70-2507/1/202211Other Types of ScoringAttritionAuthori

13、sationsRecoveryResponse ProfitabilityCustomer 7/1/202212Presentation TopicsOverview of scoring Business ObjectivesWorld BanksMonitoringFuture DirectionScorecard Modelling7/1/202213Good/Bad OddsA scoring system does not individually identify a good performer from a bad performer, it classifies an app

14、licant in a particular “Good/Bad odds” group.An applicant belonging to a 200 to 1 group, appears pretty safe and profitable.If the applicant belongs to a 4 to 1 risk group, we would no doubt find the risk unacceptable. There is a “cut-off” point where it is not profitable for the bank to accept a ce

15、rtain Good to Bad ratioBased on the above, it is accepted that there will be some “bads” above the cut-off level set, and some “goods” below the cut-off level set.7/1/202214Good/Bad DiscriminationThe objective of a scorecard is to have characteristics which discriminate between Good and Bad accounts

16、 with a sufficiently high probability. Some characteristics are legally or ethically not usedThe score will be a measure of the probability of being a Good or Bad performer.If the scorecard is performing well then the average scores of Bads are lower than the average scores of the Goods.7/1/202215Pe

17、rformance ChartsThe Good/Bad Odds at each score can be determined and plotted onto a Performance chart04080120160200240280320360400440480520560600640680720760800ScoreNumberOf ClientsGoodsBads81Graph 2 - Log Odds Performance Chart05251286453250164000408012016020024028032036040044048052056060064068072

18、0760800Good/Bad Odds02468101214Log GBOs (Base 2)8 to 12 to 137/1/202216Application Scorecard Construction Flow ChartCharacteristic AnalysisMultivariate model buildReject InferenceStatistical AnalysisCustomised ScorecardProduct IdentificationFile Data AvailabilitySamplingData Extraction/CostData Inte

19、gritySet cut-off ScoreImplementationValidationGeneric ScorecardExternal Data SourceScorecard VendorOutsourcingScorecard Monitoring7/1/202217Model BuildOnce the characteristics have been selected a statistical model can be developed.Multivariate statistical methods include Logistic RegressionStepwise

20、 methodsResidual analysisNot all predictive characteristics are used in the model.An inter-correlation effect may exist between variables. For example, age may be correlated with time at current employment and therefore only one is necessary in the model.7/1/202218ModelsExpert SystemsDecision TreesL

21、inear RegressionLogistic Regression has the following form:Neural Networks7/1/202219Model BuildThe model is built on dichotomous data. In this case a 1 for “Good” customers and a 0 for “Bad” customers.7/1/202220Logistic RegressionThe logistic regression fits the probability better than Linear regres

22、sion.7/1/202221Reject Inference and ValidationReject InferenceReject Inference is only necessary for scorecards were there is no performance information for rejected applicationsApplications that are rejected must be included in the final model. Behavioural scorecards deal only in existing customers

23、, therefore do not require reject inference.ValidationA randomly selected control group (hold out sample) or proxy portfolio to test the model.7/1/202222Measures of discriminationReceiver Operating Curve (ROC)The Receiver Operating Curve is the area under the curve generated when the cumulative Bads

24、 are plotted against the cumulative goods (Lorenz Curve).Gini coefficient (G)This discrimination measure is geometrically defined as the ratio of the area A of the shaded semi-circular area to the area B of the triangle in the Lorenz diagram. PH (percentage Good for 50% Bad)This is defined as the cu

25、mulative proportion of Goods up to the median value of the Bads. Gini.xls7/1/202223Scorecard performance can be judged on the level of discriminationTwo measure that can be used are:Gini (or ROC)PH - % of Goods below 50% of bads1% of PH could mean an additional 3% approvals1% of PH could mean an red

26、uction of 0.2% bad debtsGini=0.62%Measures of discrimination (I)7/1/202224Measures of discrimination (II)Discrimination measures should be determined for discrete attributesChi-SquaredFico (Kullback Divergence)Based on a book by Solomon Kullback“Information Theory and Statistics”7/1/202225Issues for

27、 Successful ImplementationCultural ChangeRequires top management supportOperational processRedesign to minimise manual intervention and maximise cost savings.Data IntegrityQuality of the overall decisions, and subsequently the Portfolio, is dependant upon the accuracy of the data input. The first ti

28、me!Setting the Cut-off score correctly7/1/202226Presentation TopicsOverview of scoringScorecard ModellingWorld BanksMonitoringFuture Direction Business Objectives7/1/202227Business ObjectivesIncrease consistency of lending decisionsConsistent & unbiased treatment of applicant Customers with the same

29、 details get the same scoreTotal management control over credit approval systemsAllows for loosening or tightening of lending through credit cycles Potential increase in approvalsReduce operating costsIncrease in automated processingImprove customer serviceFast and consistent decisions at applicatio

30、n pointMore appropriate limit and authorisation decisionsReduction in collection actions on low risk accountsRisk based allocation of credit limits and issue terms7/1/202228Business Objectives (cont)Improved portfolio managementManage credit portfolios more effectively and dynamicallyBetter predicti

31、on of credit lossesManagement ability to react to changes fast & accurately Ability to measure & forecast impact of policy decisionsQuick and uniform policy implementationImproved Management Information Systems (MIS)Permits MIS to be developed to assist business needs and marketing activitiesMIS can

32、 be fed back into future scorecard developments and collection activities7/1/202229Presentation TopicsOverview of scoringScorecard Modelling Business ObjectivesMonitoringFuture DirectionWorld Banks7/1/202230World BanksANZEuropean BanksBanking market in Europe is restructuringBanks are merging across

33、 country boundariesUK bank visitsBank A - bank with many recent acquisitionsBank B - bank dealing with mainly credit cardsBank C - ex building society now owned by bankBank D - large diverse bankNational Australia Bank7/1/202231World BanksUK BanksAUS Banks7/1/202232BureausFair Isaac is the main bure

34、aus in USA“White” and “Black” data is supplied to and from all financial institution Fair Isaac (Equifax) and Experian are the two main bureaus in UK“White” data is supplied to a financial institution if the supply to bureauCurrently few banks supply and receive “white” dataMergers are leading most

35、banks to look at this optionFair Isaac is trying to beat Experian in having bureau scores in the UKThis is only possible when all banks supply “white” dataCredit Advantage is used in AustraliaProvides “Black” data onlyLinked with Decision Advantage (previously Equigen)Bureau scores used for ANZ Smal

36、l BusinessWe could use Dunn & Bradstreet for over $250k lendingBaycorp is used in New ZealandProvides “Black” data onlyBaycorp is also a collections agencyNZ puts the smallest amount lost as a defaultBaycorp and Credit Advantage have just merged7/1/202233Credit Scoring & Bureaus Around the World“We

37、are not alone!”BBBBBBB7/1/202234BASEL - The New AccordThe New Accord will give banks with sophisticated risk management capabilities increased flexibilityMore emphasis on banks internal measures of risk, supervisory review and market disciplineDecision support technology has an important role to pla

38、yIncentivise better risk management Data warehouses are fundamental to addressing many of the requirementsSMB sector will be key More risk sensitiveCompetitive equalityPaul%20Russell%2013a1The New Basel Capital AccordPillar 1 : Minimum capital requirementPillar 2 : SupervisoryreviewprocessPillar 3 :

39、 Market discipline7/1/202235Pillar 1 : credit riskInternal Rating Based (IRB) approachFoundationBank sets Probability of Default (PD)Standard Exposure At Default (EAD)Standard Loss Given Default (LGD)AdvancedBanks sets PD, EAD & LGDBetter recognition of credit risk mitigation techniquesBehavioural s

40、coringInternalExternal Data storage7/1/202236Future direction of scoring“Adaptive Control” first implemented 1985 in USAChampion/Challenger processes for determining actions based on scoresRequired 10 years to be widespread in USCustomer Relationship ManagementProfitability (NIACC)AttritionPropensit

41、y to Buy (Cross Sell) Life time revenueRecovery scorecardsOperations Research MethodsSimulation modelling7/1/202237Presentation TopicsOverview of scoringScorecard Modelling Business ObjectivesWorld BanksFuture DirectionMonitoring7/1/202238Monitoring Examples1. Operation Stability Reports The four ty

42、pes of front end monitoring reports:1.1 Approval Statistics Report1.2 Population Stability Report1.3 System Rules Referral Report1.4 Portfolio Statistics ReportOperational statistics can be obtained as soon as an automated decision process is implementedEarly warning indicators of decision functiona

43、lity error and scorecard validityShould be produced by Business Units or MISLoan Approval/Declines by ScoreApprova/Declinal Rates by Score0%10%20%30%40%50%60%70%80%90%100%1000Score BandsPercentagesAuto DeclinedManually DeclinedManually ApprovedAuto Approved7/1/202240Population StabilityCompare each

44、characteristic and attribute over time against benchmarksPlot score distributions over time for potential changeIndicates potential drift in performance NOYESDec-9625%75%Mar-9723%77%Jun-9724%76%Sep-9722%78%Dec-9721%79%Mar-9819%81%Jun-9819%81%Sep-9822%78%Dec-9820%80%Mar-9920%80%Jun-9918%82%Sep-9918%8

45、2%Dec-9917%83%Benchmarks29%71%Population Stability0%10%20%30%40%50%60%70%80%90%NOYESDec-96Mar-97Jun-97Sep-97Dec-97Mar-98Jun-98Sep-98Dec-98Mar-99Jun-99Sep-99Dec-997/1/202241Monitoring Requirements2. Performance AnalysisThe two types of back end monitoring are:2.1 Scorecard Performance Report2.2 Chara

46、cteristic Analysis Report2.3 Dynamic Delinquency ReportPerformance Analysis is undertaken once a certain level of customer maturity has been establishedShould be produced by BU and Group RiskLoans - Approval & Delinquency RatesEven with manual assessment below the cut-off score of 350 the delinquenc

47、y rates are higherLoans Approval & Delinquency Rates0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1-300301-350351-400401-450451-500501-550551-600601-650651-700701-750751-800800ScoreApproval Rates0% 5% 10% 15% 20% 25% Delinquency Rates% Approved (LHS)Delinquency Rates (RHS)7/1/202243Scorecard Performan

48、ceScorecard performance based on 30+ delinquencyGood/Bad odds increase as expected by score Score Distribution & G/B Odds050010001500200025003000350040001000Score0.05.010.015.020.025.030.035.040.0Non DelinqDelinqHL GB Odds7/1/202244Presentation TopicsOverview of scoringScorecard Modelling Business ObjectivesWorld BanksMonitoringFuture Direction7/1/202245Future DirectionModellingExperimental DesignChampion/Challenger StrategiesHypothesis testing (uni & multi- dime