Transcript shit
16/11/08
Automated Credit Scoring: The Necessary Next Step
Dr Howard Haughton Holistic Risk Solutions Limited These slides are the Copyright of Holistic Risk Solutions Limited 1
Contents
What is Credit Scoring Factors influencing increased borrowing Main types of scoring Benefits of scoring Pitfalls of scoring Challenges to implementation Capturing characteristics Discriminant/Logit analysis Judgmental scoring Validating scoring models Integrating credit scoring models into loan approval process 16/11/08 These slides are the Copyright of Holistic Risk Solutions Limited 2
What is credit scoring - quick review
A
quantitative
determine whether to if so,
how much
technique used to
extend
credit (and ) to a borrower.
16/11/08 These slides are the Copyright of Holistic Risk Solutions Limited 3
Factors influencing increased consumer borrowing
More diversified use of credit cards Cards being used to buy a cappuccino at coffee houses Petrol at gas stations Lower minimum payments Due to competitive pressures greater incentives being offered by lenders Lowering of down payments Zero (0%) down on real-estate and automobiles Increased risk tolerance Niche market …high potential returns for risky lending e.g. sub prime 16/11/08 These slides are the Copyright of Holistic Risk Solutions Limited 4
Main types of scoring
Application Scoring (either judgmental or statistical) Mechanisms used to determine whether or not credit should be extended to an applicant Behavioral Scoring (analytical) Mechanisms used to predict different types of behavior on a credit account. Unlike application scoring, which is a one-off event, behavioral scoring provides a regular, up-to-date assessment of an account’s likely future status.
Examples include fraud, account management and collections These slides are the Copyright of Holistic Risk Solutions Limited 16/11/08 5
What’s a credit scoring system look like?
R4 R5 R6 R7
Risk Rating
R1 R2 R3 16/11/08
Description
Excellent Good Average Acceptable Marginal Substandard Doubtful
Credit Score
These slides are the Copyright of Holistic Risk Solutions Limited >891 782 - 891 673 - 782 564 - 673 455 - 564 346 - 455 237 - 346 6
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Benefits of scoring
Can be used to quantify risk as a probability to default which allows for a wider continuum of classification Consistency of scores for applicants of similar characteristics, thus removing subjectivity Can be used to accommodate a wide range of factors e.g. profession ranging through to methods of payment Can be tested and independently verified prior to being employed Facilitates meaning statistical analysis. For example analysis might reveal that the historic loss rate for those with scores lower than a certain value is 50%. This information would be a useful risk management tool (e.g. pricing) Expedites the approval process Provides the basis for the targeted marketing of new products to prospective clients Good applicants can get better rates and poorer applicants higher rates Provides a better basis for making loan provisions and determining the adequacy of economic capital These slides are the Copyright of Holistic Risk Solutions Limited 16/11/08 7
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Pitfalls of scoring
A large amount of historic loans is usually required to build scoring models. Data is required on all applications (those performing
good
or
bad
)
rejected
, as well as those that are A fairly large amount of characteristics e.g. demographic and characteristic data per loan Data quality. Incorrect capture of data can skew statistical results Undisciplined delinquency management can render the scoring unpredictable. For example a firm which doesn’t classify loans as non-performing after 90 days runs the risk of underestimating default probabilities Unscientific determination of the “cut-off” point (i.e. the score beneath which applications are rejected) can lead to too many loans being rejected (including potentially good ones) Too heavy reliance may result in cases where “mitigating” circumstances are not factored into decision making Static scoring models might not factor changes to demographics and/or macro-economic data These slides are the Copyright of Holistic Risk Solutions Limited 16/11/08 8
Challenges to Implementation
Credit reference bureau Completeness and accuracy of data (i.e. characteristics used in developing credit scores) 16/11/08 These slides are the Copyright of Holistic Risk Solutions Limited 9
Credit reference bureau
Many developing economies have no formalized bureau. Some reasons for this being: No clear legislative process to facilitate sharing of credit information between lenders No structure to support the physical collation of data that could be exchanged between lenders and a central repository Cultural. Some see the matter as being unconstitutional and believe that relationship lending is sufficient These slides are the Copyright of Holistic Risk Solutions Limited 16/11/08 10
Capturing characteristics
Characteristics are initially chosen on the basis of the information captured on a credit application form (and/or external information such as credit reference bureau data, court judgments, if available). An example of a characteristic therefore might be the age of an applicant.
Often times, application forms do not contain either complete and/or accurate data…I once came across an applicant that was 13 years old and another that was over 900 years!
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Functional form of a score
A credit score can be formally represented as shown:
S
w
1
C
1
w
2
C
2
w n C n
The w’s denote weights and the C’s denote characteristics. Weights can be determined either judgmentally or scientifically.
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Analysis of characteristics
The purpose of analysing the characteristic is to identify those that can separate out the goods from bads. A predictive characteristic contains attributes that display very different levels of risk for the different attributes.
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Age
18-21 22-33 34+ Total 16/11/08
A closer look at Age
Total
1619 8084 28317
Good
1158 6050 22144 38020 29352 These slides are the Copyright of Holistic Risk Solutions Limited 2378
Bad
244 849 1285
Bad Rate
15.07% 10.50% 4.54% 6.25% 14
Assigning a score
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Age
18-21 22-33 34+
Score
10 20 These slides are the Copyright of Holistic Risk Solutions Limited 30 15
Determining good from bad
A scorecard is built principally on an analysis of good payers versus bad payers. There is no universal definition of what constitutes good/bad but an often used definition is: Good: never delinquent or worst delinquency is one payment down; Bad: 90 (or more) days in delinquency with payments These slides are the Copyright of Holistic Risk Solutions Limited 16/11/08 16
Determining the weights
Either a judgmental or mathematical approach can be used. A number of different mathematical approaches exist including Discriminant and Logistic.
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Discriminant analysis
Discriminant analysis provides a statistical method of finding the combination of variables that best separates the bad and good group of applicants.
The idea is to determine the vector of weights that maximise the difference between the goods and bads in the expression as given by
M
M
w T m G
w T
Sw
m B
0 .
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Logistic regression
An alternative to the discriminant analysis is to use a logistic regression model: The probabilities correspond to the probability that an applicant i has defaulted (which is relatively easy to derive from the historical data)
p i
e w
.
c
1
e w
.
c
Maximum likelihood estimation is used to derive the weights These slides are the Copyright of Holistic Risk Solutions Limited 16/11/08 19
Judgmental scoring
An example of the application of a judgmental score approach is, for example, a rule asserting that: women are better at paying their debts than men. As a consequence a woman would be assigned a score representing a better rating than that of a man.
The older the person the more likely they are to repay their debts.
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Accommodation type
Home owner With parents 50 38 Tenant 30
Time with bank
<1 year 20
Years at current address
<3 years 25 Other 30 1-3 years 28 4-9 years 35 10+ years 48 4-8 years 30 9-14 years 32 15+ years 35
Gender Age
Male 20 18-25 10 Female 30 25-35 20 35-50 30 50-65 40 >65 50 16/11/08 These slides are the Copyright of Holistic Risk Solutions Limited 21
Judgmental scoring illustrated
It can be seen that the “best” type of applicant is: One that owns a home at the time of applying Has a relationship with their bank for greater than 10 years Has lived at their current address for greater than 15 years Is female Is older than 65 These slides are the Copyright of Holistic Risk Solutions Limited 16/11/08 22
Illustrated scoring continued
It can be seen that the “worst” type of applicant is: Male Less than 1 year banking relationship Less than 3 years at their current address Is either a tenant or lodger (not with parents) Is aged between 18 and 25 These slides are the Copyright of Holistic Risk Solutions Limited 16/11/08 23
Validating the scoring model
Scoring models need to be validated to ensure that they are still applicable to current demographics and/or macro economic circumstances.
Automated techniques can be used to determine whether scoring models are still predictable e.g. Gini coefficients.
Such an implementation requires the periodic “recalculation” of the credit scores and assessing whether any statistically significant divergences exist between the old and new scoring models and making the necessary updates.
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Integrating automated scoring into existing business processes
Ideally, automated scoring systems should be integrated into the business processes supporting the loan origination process Integration would be significantly enhanced by making use of workflow tools that capture loan application details and eliminate a significant amount of paper trail Data mapping & conversion tools required to combine data from disparate systems Business rules (both low and meta-level) required to make inferences and support management decisions These slides are the Copyright of Holistic Risk Solutions Limited 16/11/08 25
Conclusions
An increasing appetite for borrowing necessitates the use of sophisticated techniques to aid in the job of quickly assessing credit risk Increases in delinquency rates across the region suggests that traditional lending techniques have not helped to produce desired RAROC levels Institutions can increase their level of competitiveness by tailoring products to customers based on their risk characteristics…a win for both lender and customer These slides are the Copyright of Holistic Risk Solutions Limited 16/11/08 26