16/11/08 These slides are the Copyright of Holistic Risk Solutions Limited1 Automated Credit Scoring: The Necessary Next Step Dr Howard Haughton Holistic

16/11/08 These slides are the Copyright of Holistic Risk Solutions Limited

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Automated Credit Scoring: The Automated Credit Scoring: The Necessary Next StepNecessary Next Step

Dr Howard HaughtonDr Howard Haughton

Holistic Risk Solutions LimitedHolistic Risk Solutions Limited


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ContentsContents What is Credit ScoringWhat is Credit Scoring Factors influencing increased borrowingFactors influencing increased borrowing Main types of scoringMain types of scoring Benefits of scoringBenefits of scoring Pitfalls of scoringPitfalls of scoring Challenges to implementationChallenges to implementation Capturing characteristicsCapturing characteristics Discriminant/Logit analysisDiscriminant/Logit analysis Judgmental scoringJudgmental scoring Validating scoring modelsValidating scoring models Integrating credit scoring models into loan approval Integrating credit scoring models into loan approval

processprocess


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What is credit scoring - quick reviewWhat is credit scoring - quick review

A A quantitativequantitative technique used to technique used to determine whether to determine whether to extendextend credit credit (and if so, (and if so, how muchhow much) to a borrower.) to a borrower.


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Factors influencing increased Factors influencing increased consumer borrowingconsumer borrowing

More diversified use of credit cardsMore diversified use of credit cards Cards being used to buy a cappuccino at coffee housesCards being used to buy a cappuccino at coffee houses Petrol at gas stationsPetrol at gas stations

Lower minimum paymentsLower minimum payments Due to competitive pressures greater incentives being Due to competitive pressures greater incentives being

offered by lendersoffered by lenders

Lowering of down paymentsLowering of down payments Zero (0%) down on real-estate and automobilesZero (0%) down on real-estate and automobiles

Increased risk toleranceIncreased risk tolerance Niche market …high potential returns for risky lending e.g. Niche market …high potential returns for risky lending e.g.

sub-primesub-prime


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Main types of scoringMain types of scoring Application Scoring (either judgmental or statistical)Application Scoring (either judgmental or statistical)

Mechanisms used to determine whether or not credit Mechanisms used to determine whether or not credit should be extended to an applicantshould be extended to an applicant

Behavioral Scoring (analytical)Behavioral Scoring (analytical) Mechanisms used to predict different types of Mechanisms used to predict different types of

behavior on a credit account.behavior on a credit account. Unlike application Unlike application scoring, which is a one-off event, behavioral scoring scoring, which is a one-off event, behavioral scoring provides a regular, up-to-date assessment of an provides a regular, up-to-date assessment of an account’s likely future status.account’s likely future status.

Examples include fraud, account management and Examples include fraud, account management and collectionscollections


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What’s a credit scoring system look like?What’s a credit scoring system look like?

Risk Rating Description Credit Score

R1 Excellent

>891

R2 Good

782 - 891

R3 Average

673 - 782

R4 Acceptable

564 - 673

R5 Marginal

455 - 564

R6 Substandard

346 - 455

R7 Doubtful

237 - 346


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Benefits of scoringBenefits of scoring1.1. Can be used to quantify risk as a probability to default which allows Can be used to quantify risk as a probability to default which allows

for a wider continuum of classificationfor a wider continuum of classification2.2. Consistency of scores for applicants of similar characteristics, thus Consistency of scores for applicants of similar characteristics, thus

removing subjectivityremoving subjectivity3.3. Can be used to accommodate a wide range of factors e.g. profession Can be used to accommodate a wide range of factors e.g. profession

ranging through to methods of paymentranging through to methods of payment4.4. Can be tested and independently verified prior to being employedCan be tested and independently verified prior to being employed5.5. Facilitates meaning statistical analysis. For example analysis might Facilitates meaning statistical analysis. For example analysis might

reveal that the historic loss rate for those with scores lower than a reveal that the historic loss rate for those with scores lower than a certain value is 50%. This information would be a useful risk certain value is 50%. This information would be a useful risk management tool (e.g. pricing)management tool (e.g. pricing)

6.6. Expedites the approval processExpedites the approval process7.7. Provides the basis for the targeted marketing of new products to Provides the basis for the targeted marketing of new products to

prospective clientsprospective clients8.8. Good applicants can get better rates and poorer applicants higher Good applicants can get better rates and poorer applicants higher

ratesrates9.9. Provides a better basis for making loan provisions and determining Provides a better basis for making loan provisions and determining

the adequacy of economic capitalthe adequacy of economic capital


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Pitfalls of scoringPitfalls of scoring1.1. A large amount of historic loans is usually required to build scoring A large amount of historic loans is usually required to build scoring

models. Data is required on all applications (those models. Data is required on all applications (those rejectedrejected, as , as well as those that are performing well as those that are performing goodgood or or badbad))

2.2. A fairly large amount of characteristics e.g. demographic and A fairly large amount of characteristics e.g. demographic and characteristic data per loancharacteristic data per loan

3.3. Data quality. Incorrect capture of data can skew statistical results Data quality. Incorrect capture of data can skew statistical results 4.4. Undisciplined delinquency management can render the scoring Undisciplined delinquency management can render the scoring

unpredictable. For example a firm which doesn’t classify loans as unpredictable. For example a firm which doesn’t classify loans as non-performing after 90 days runs the risk of underestimating non-performing after 90 days runs the risk of underestimating default probabilitiesdefault probabilities

5.5. Unscientific determination of the “cut-off” point (i.e. the score Unscientific determination of the “cut-off” point (i.e. the score beneath which applications are rejected) can lead to too many loans beneath which applications are rejected) can lead to too many loans being rejected (including potentially good ones) being rejected (including potentially good ones)

6.6. Too heavy reliance may result in cases where “mitigating” Too heavy reliance may result in cases where “mitigating” circumstances are not factored into decision makingcircumstances are not factored into decision making

7.7. Static scoring models might not factor changes to demographics Static scoring models might not factor changes to demographics and/or macro-economic dataand/or macro-economic data


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Challenges to ImplementationChallenges to Implementation

Credit reference bureauCredit reference bureau Completeness and accuracy of data Completeness and accuracy of data

(i.e. characteristics used in (i.e. characteristics used in developing credit scores)developing credit scores)


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Credit reference bureauCredit reference bureau

Many developing economies have no formalized Many developing economies have no formalized bureau. Some reasons for this being:bureau. Some reasons for this being: No clear legislative process to facilitate sharing of No clear legislative process to facilitate sharing of

credit information between lenderscredit information between lenders No structure to support the physical collation of data No structure to support the physical collation of data

that could be exchanged between lenders and a that could be exchanged between lenders and a central repositorycentral repository

Cultural. Some see the matter as being Cultural. Some see the matter as being unconstitutional and believe that relationship lending unconstitutional and believe that relationship lending is sufficientis sufficient


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Capturing characteristicsCapturing characteristics

Characteristics are initially chosen on the basis Characteristics are initially chosen on the basis of the information captured on a credit of the information captured on a credit application form (and/or external information application form (and/or external information such as credit reference bureau data, court such as credit reference bureau data, court judgments, if available). An example of a judgments, if available). An example of a characteristic therefore might be the age of an characteristic therefore might be the age of an applicant.applicant.

Often times, application forms do not contain Often times, application forms do not contain either complete and/or accurate data…I once either complete and/or accurate data…I once came across an applicant that was 13 years old came across an applicant that was 13 years old and another that was over 900 years!and another that was over 900 years!


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Functional form of a scoreFunctional form of a score

A credit score can be formally represented A credit score can be formally represented as shown:as shown:

The w’s denote weights and the C’s denote The w’s denote weights and the C’s denote characteristics. Weights can be characteristics. Weights can be determined either judgmentally or determined either judgmentally or scientifically.scientifically.

nnCwCwCwS 2211


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Analysis of characteristicsAnalysis of characteristics

The purpose of analysing the The purpose of analysing the characteristic is to identify those that characteristic is to identify those that can separate out the goods from can separate out the goods from bads. A predictive characteristic bads. A predictive characteristic contains attributes that display very contains attributes that display very different levels of risk for the different levels of risk for the different attributes.different attributes.


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A closer look at AgeA closer look at Age

Age Total Good Bad Bad Rate

18-21 1619 1158 244 15.07%

22-33 8084 6050 849 10.50%

34+ 28317 22144 1285 4.54%

Total 38020 29352 2378 6.25%


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Assigning a scoreAssigning a score

Age Score

18-21 10

22-33 20

34+ 30


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Determining good from badDetermining good from bad

A scorecard is built principally on an analysis of A scorecard is built principally on an analysis of good payers versus bad payers. There is no good payers versus bad payers. There is no universal definition of what constitutes universal definition of what constitutes good/bad but an often used definition is:good/bad but an often used definition is: GoodGood: never delinquent or worst delinquency : never delinquent or worst delinquency

is one payment down;is one payment down; BadBad: 90 (or more) days in delinquency with : 90 (or more) days in delinquency with

paymentspayments


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Determining the weightsDetermining the weights

Either a judgmental or mathematical approach Either a judgmental or mathematical approach can be used. A number of different can be used. A number of different mathematical approaches exist including mathematical approaches exist including Discriminant and Logistic.Discriminant and Logistic.


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Discriminant analysisDiscriminant analysis

Discriminant analysis Discriminant analysis provides a statistical provides a statistical method of finding the method of finding the combination of variables combination of variables that best separates the that best separates the bad and good group of bad and good group of applicants.applicants.

The idea is to determine The idea is to determine the vector of weights that the vector of weights that maximise the difference maximise the difference between the goods and between the goods and bads in the expression as bads in the expression as given by given by MM

5.0Sww

mmwM

T

BGT


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Logistic regressionLogistic regression An alternative to the An alternative to the

discriminant analysis is to discriminant analysis is to use a logistic regression use a logistic regression model:model:

The probabilities The probabilities correspond to the correspond to the probability that an probability that an applicant applicant i has defaulted i has defaulted (which is relatively easy to (which is relatively easy to derive from the historical derive from the historical data)data)

Maximum likelihood Maximum likelihood estimation is used to derive estimation is used to derive the weightsthe weights

cw

cw

i e

ep

.

.

1


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Judgmental scoringJudgmental scoring

An example of the application of a judgmental An example of the application of a judgmental score approach is, for example, a rule asserting score approach is, for example, a rule asserting that:that:

women are better at paying their debts than women are better at paying their debts than men. As a consequence a woman would be men. As a consequence a woman would be assigned a score representing a better rating assigned a score representing a better rating than that of a man.than that of a man.

The older the person the more likely they are The older the person the more likely they are to repay their debts.to repay their debts.


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Accommodation Accommodation typetype

Home ownerHome owner With parentsWith parents TenantTenant OtherOther

5050 3838 3030 3030

Time with bankTime with bank <1 year<1 year 1-3 years1-3 years 4-9 years4-9 years 10+ years10+ years

2020 2828 3535 4848

Years at current Years at current addressaddress

<3 years<3 years 4-8 years4-8 years 9-14 years9-14 years 15+ years15+ years

2525 3030 3232 3535

GenderGender MaleMale FemaleFemale

2020 3030

AgeAge 18-2518-25 25-3525-35 35-5035-50 50-6550-65 >65>65

1010 2020 3030 4040 5050


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Judgmental scoring illustratedJudgmental scoring illustrated

It can be seen that the “best” type of It can be seen that the “best” type of applicant is:applicant is: One that owns a home at the time of applyingOne that owns a home at the time of applying Has a relationship with their bank for greater Has a relationship with their bank for greater

than 10 yearsthan 10 years Has lived at their current address for greater Has lived at their current address for greater

than 15 yearsthan 15 years Is femaleIs female Is older than 65 Is older than 65


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Illustrated scoring continuedIllustrated scoring continued

It can be seen that the “worst” type It can be seen that the “worst” type of applicant is:of applicant is: MaleMale Less than 1 year banking relationshipLess than 1 year banking relationship Less than 3 years at their current Less than 3 years at their current

addressaddress Is either a tenant or lodger (not with Is either a tenant or lodger (not with

parents)parents) Is aged between 18 and 25Is aged between 18 and 25


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Validating the scoring modelValidating the scoring model Scoring models need to be validated to ensure that they Scoring models need to be validated to ensure that they

are still applicable to current demographics and/or are still applicable to current demographics and/or macro economic circumstances.macro economic circumstances.

Automated techniques can be used to determine Automated techniques can be used to determine whether scoring models are still predictable e.g. Gini whether scoring models are still predictable e.g. Gini coefficients.coefficients.

Such an implementation requires the periodic Such an implementation requires the periodic “recalculation” of the credit scores and assessing “recalculation” of the credit scores and assessing whether any statistically significant divergences exist whether any statistically significant divergences exist between the old and new scoring models and making the between the old and new scoring models and making the necessary updates.necessary updates.


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Integrating automated scoring into Integrating automated scoring into existing business processesexisting business processes

Ideally, automated scoring systems should be Ideally, automated scoring systems should be integrated into the business processes supporting integrated into the business processes supporting the loan origination processthe loan origination process

Integration would be significantly enhanced by Integration would be significantly enhanced by making use of workflow tools that capture loan making use of workflow tools that capture loan application details and eliminate a significant application details and eliminate a significant amount of paper trailamount of paper trail

Data mapping & conversion tools required to Data mapping & conversion tools required to combine data from disparate systemscombine data from disparate systems

Business rules (both low and meta-level) required Business rules (both low and meta-level) required to make inferences and support management to make inferences and support management decisionsdecisions


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Conclusions Conclusions An increasing appetite for borrowing necessitates the An increasing appetite for borrowing necessitates the

use of sophisticated techniques to aid in the job of use of sophisticated techniques to aid in the job of quickly assessing credit riskquickly assessing credit risk

Increases in delinquency rates across the region Increases in delinquency rates across the region suggests that traditional lending techniques have not suggests that traditional lending techniques have not helped to produce desired RAROC levelshelped to produce desired RAROC levels

Institutions can increase their level of competitiveness Institutions can increase their level of competitiveness by tailoring products to customers based on their risk by tailoring products to customers based on their risk characteristics…a win for both lender and customercharacteristics…a win for both lender and customer

Documents

16/11/08 These slides are the Copyright of Holistic Risk Solutions Limited1 Automated Credit Scoring: The Necessary Next Step Dr Howard Haughton Holistic