Credit scoring for profitability objectives

In consumer credit markets lending decisions are usually represented as a set of classification problems. The objective is to predict the likelihood of customers ending up in one of a finite number of states, such as good/bad payer, responder/non-responder and transactor/non-transactor. Decision rules are then applied on the basis of the resulting model estimates. However, this represents a misspecification of the true objectives of commercial lenders, which are better described in terms of continuous financial measures such as bad debt, revenue and profit contribution. In this paper, an empirical study is undertaken to compare predictive models of continuous financial behaviour with binary models of customer default. The results show models of continuous financial behaviour to outperform classification approaches. They also demonstrate that scoring functions developed to specifically optimize profit contribution, using genetic algorithms, outperform scoring functions derived from optimizing more general functions such as sum of squared error.

[1]  Terence C. Fogarty,et al.  Evolving Bayesian classifiers for credit control—a comparison with other machine-learning methods , 1993 .

[2]  S. M. Finlay.  Towards profitability: a utility approach to the credit scoring problem , 2008, J. Oper. Res. Soc..

[3]  DJ Hand,et al.  Performance criteria for plastic card fraud detection tools , 2008, J. Oper. Res. Soc..

[4]  R. Fildes,et al.  Forecasting and loss functions , 1988 .

[5]  George A. Overstreet,et al.  The flat-maximum effect and generic linear scoring models: a test , 1992 .

[6]  R. M. Oliver,et al.  Optimal scoring cutoff policies and efficient frontiers , 2005, J. Oper. Res. Soc..

[7]  Klaus Reiner Schenk-Hoppé,et al.  Forecasting Customer Behaviour in a Multi-Service Financial Organisation: A Profitability Perspective , 2012 .

[8]  D. J. Hand,et al.  Good practice in retail credit scorecard assessment , 2005, J. Oper. Res. Soc..

[9]  Steven Finlay.  Consumer Credit Fundamentals , 2005 .

[10]  David Coley,et al.  Introduction to Genetic Algorithms for Scientists and Engineers , 1999 .

[11]  Maria Stepanova,et al.  Survival Analysis Methods for Personal Loan Data , 2002, Oper. Res..

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  Mary A. Hopper,et al.  Development and use of credit profit measures for account management , 1992 .

[14]  A. Lucas,et al.  Statistical challenges in credit card issuing , 2001 .

[15]  David J. Hand,et al.  Optimal bipartite scorecards , 2005, Expert Syst. Appl..

[16]  Steven Finlay,et al.  Using genetic algorithms to develop scoring models for alternative measures of performance , 2005 .

[17]  Bart Baesens,et al.  Using Neural Network Rule Extraction and Decision Tables for Credit - Risk Evaluation , 2003, Manag. Sci..

[18]  Jonathan N. Crook,et al.  Credit Scoring and Its Applications , 2002, SIAM monographs on mathematical modeling and computation.

[19]  Richard M. Cyert,et al.  Estimation of the Allowance for Doubtful Accounts by Markov Chains , 1962 .

[20]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[21]  Jonathan N. Crook,et al.  Recent developments in consumer credit risk assessment , 2007, Eur. J. Oper. Res..

[22]  Robert C. Blattberg,et al.  Customer Lifetime Value: Empirical Generalizations and Some Conceptual Questions , 2009 .

[23]  Johan A. K. Suykens,et al.  Benchmarking state-of-the-art classification algorithms for credit scoring , 2003, J. Oper. Res. Soc..

[24]  Robert M. Oliver,et al.  Efficient frontier cutoff policies in credit portfolios , 2001, J. Oper. Res. Soc..

[25]  David J. Hand,et al.  Statistical Classification Methods in Consumer Credit Scoring: a Review , 1997 .

[26]  Colin R. Reeves,et al.  Genetic Algorithms: Principles and Perspectives: A Guide to Ga Theory , 2002 .

[27]  Steven Finlay,et al.  Are we modelling the right thing? The impact of incorrect problem specification in credit scoring , 2009, Expert Syst. Appl..

[28]  Steven Finlay,et al.  The Management of Consumer Credit: Theory and Practice , 2008 .

[29]  Jonathan Crook,et al.  Credit Scoring Models in the Credit Union Environment Using Neural Networks and Genetic Algorithms , 1997 .

[30]  J. Crook,et al.  Credit scoring using neural and evolutionary techniques , 2000 .

[31]  John Fox,et al.  Nonparametric simple regression , 2000 .

[32]  David J. Hand,et al.  A survey of the issues in consumer credit modelling research , 2005, J. Oper. Res. Soc..

[33]  P. Lovie,et al.  The flat maximum effect and linear scoring models for prediction , 1986 .

[34]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[35]  William T. Scherer,et al.  Time will tell: behavioural scoring and the dynamics of consumer credit assessment , 2001 .

[36]  Jonathan Crook,et al.  Modelling profitability using survival combination scores , 2007, Eur. J. Oper. Res..

[37]  David J. Hand,et al.  Direct versus indirect credit scoring classifications , 2002, J. Oper. Res. Soc..

[38]  C. Reeves.  Modern heuristic techniques for combinatorial problems , 1993 .

[39]  Lyn C. Thomas,et al.  PHAB scores: proportional hazards analysis behavioural scores , 2001, J. Oper. Res. Soc..