1. Developing Intelligent Systems for Credit Scoring Using Machine Learning Techniques
PhD Committee
J. Vanthienen
(promotor, K.U.Leuven)
J. Vandenbulcke
(K.U.Leuven)
M. Verhelst
M. Vandebroek
J. Crook
(Univ. Edinburgh)
L. Thomas
(Univ. Southampton)
Bart Baesens
Public Defence
September 24th, 2003

2. Overview Knowledge Discovery in Data
KDD
Credit Scoring
Accuracy
Comprehensibility
Survival Analysis
Conclusions
Knowledge Discovery in Data
The Credit Scoring Classification Problem
Developing Accurate Credit Scoring Systems
Developing Comprehensible Credit Scoring Systems
Survival Analysis for Credit Scoring
Conclusions

3. Knowledge Discovery in Data
Overview
KDD
Credit Scoring
Accuracy
Comprehensibility
Survival Analysis
Conclusions
The data avalanche problem
finance, marketing, medicine, engineering
Knowledge Discovery in Data (KDD) aims at learning patterns from data using advanced algorithms
KDD steps
Data preprocessing
Data mining
Post processing
Machine learning provides a multitude of induction algorithms aimed at learning patterns from data

4. The Credit Scoring Classification Problem
Overview
KDD
Credit Scoring
Accuracy
Comprehensibility
Survival Analysis
Conclusions
Credit scoring is a technique that helps organizations to decide whether or not to grant credit to customers who apply for a loan.
The aim is to develop classification models based upon repayment behavior of past applicants.
These models summarize all available information of an applicant in a score P(applicant is good payer | age, marital status, savings amount, …).
If this score is above a predetermined threshold credit is granted, otherwise credit is denied.

5. Developing Accurate Credit Scoring Systems
Overview
KDD
Credit Scoring
Accuracy
Comprehensibility
Survival Analysis
Conclusions
Credit scoring systems should be able to accurately distinguish good applicants from bad applicants.
The problem is usually tackled using classification techniques.
E.g., logistic regression, discriminant analysis, decision trees, Bayesian networks, neural networks, support vector machines, k-nearest neighbor, …
Benchmarking study
Income > $50,000
Job > 3 Years
High Debt No
Good Risk
Yes
Bad Risk

6. Developing Accurate Credit Scoring Systems (contd.)
Overview
KDD
Credit Scoring
Accuracy
Comprehensibility
Survival Analysis
Conclusions
Experimental setup
8 real-life credit scoring data sets
Various cut-off setting schemes
Classification accuracy + Area under Receiver Operating Characteristic Curve
McNemar test + DeLong, DeLong and Clarke-Pearson test
Conclusions
Flat maximum effect
Non-linear classifiers perform consistently good, however simple, linear classifiers also give good performance
Only a handful of techniques were clearly inferior

7. Developing Comprehensible Credit Scoring Systems
Overview
KDD
Credit Scoring
Accuracy
Comprehensibility
Survival Analysis
Conclusions
Ideally, a credit scoring system should be easy to understand and implement.
“What is needed, clearly, is a redirection of credit scoring research efforts toward development of explanatory models of credit performance and the isolation of variables bearing an explanatory relationship to credit performance” (Capon, 1982)
Legally and ethically justified (e.g. Equal Credit Opportunities Act in US)
Trade-off between accuracy and comprehensibility (Occam’s Razor)
Pluralitas non est ponenda sine neccesitate
William of Occam (ca )

8. Developing Comprehensible Credit Scoring Systems (contd.)
Overview
KDD
Credit Scoring
Accuracy
Comprehensibility
Survival Analysis
Conclusions
Neural network rule extraction
Rule representation formalisms
Propositional rule
If purpose=cash and Savings Account ≤ 50€ Then Applicant=bad
Oblique rule
If 0.84Income Savings Account ≤ 1000€ Then Applicant=bad
M-of-N rules
If {at least/exactly/at most} M of the N conditions (C1,C2,..,CN) are satisfied Then Applicant=bad
Descriptive fuzzy rules
If percentage of financial burden is large Then Applicant=bad
Approximate fuzzy rules
If term is trapezoidal( ) Then Applicant=bad

9. Developing Comprehensible Credit Scoring Systems (contd.)
Overview
KDD
Credit Scoring
Accuracy
Comprehensibility
Survival Analysis
Conclusions

10. Developing Comprehensible Credit Scoring Systems (contd.)
Overview
KDD
Credit Scoring
Accuracy
Comprehensibility
Survival Analysis
Conclusions

11. Developing Comprehensible Credit Scoring Systems (contd.)
Overview
KDD
Credit Scoring
Accuracy
Comprehensibility
Survival Analysis
Conclusions

12. Survival Analysis for Credit Scoring
Overview
KDD
Credit Scoring
Accuracy
Comprehensibility
Survival Analysis
Conclusions
Predict when customers default
Implications for profit scoring and debt provisioning
Censored data
Statistical models for survival analysis
E.g. Kaplan-Meier, parametric models, proportional hazards
Drawbacks
Linear relationships
No interaction effects
Proportional hazards assumption

13. Survival Analysis for Credit Scoring (contd.)
Overview
KDD
Credit Scoring
Accuracy
Comprehensibility
Survival Analysis
Conclusions
Neural networks for survival analysis
Requirements
Monotonically decreasing survival curve
Scalable
Censoring
Empirically tested for predicting default and early repayment
Comparisons with proportional hazards models

14. Conclusions Developing accurate credit scoring systems
Overview
KDD
Credit Scoring
Accuracy
Comprehensibility
Survival Analysis
Conclusions
Developing accurate credit scoring systems
Flat maximum effect
Superiority of non-linear classifiers
Satisfactory performance of linear classifiers
Developing comprehensible credit scoring systems
Neural network rule extraction
Decision tables
Fuzzy rule extraction
Neural network survival analysis

15. Future Research Indirect Credit Scoring Knowledge Fusion
Overview
KDD
Credit Scoring
Accuracy
Comprehensibility
Survival Analysis
Conclusions
Indirect Credit Scoring
Knowledge Fusion
Behavioral Credit Scoring
Extensions to other Contexts and Problem Domains