Measuring customer quality in retail banking

The retail banking sector makes heavy use of statistical models to predict various aspects of customer behaviour. These models are built using data from earlier customers, but have several weaknesses. An alternative approach, widely used in social measurement, but apparently not yet applied in the retail banking sector, is to use latent-variable techniques to measure the underlying key aspect of customer behaviour. This paper describes such a model that separates the observed variables for a customer into primary characteristics on the one hand, and indicators of previous behaviour on the other, and links the two via a latent variable that we identify as ‘customer quality’. We describe how to estimate the conditional distribution of customer quality, given the observed values of primary characteristics and past behaviour.

[1]  D. Hand,et al.  A Discrete Variable Chain Graph for Applicants for Credit , 1999 .

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

[3]  So Young Sohn,et al.  Reject inference in credit operations based on survival analysis , 2006, Expert Syst. Appl..

[4]  L. Ryan,et al.  Latent Variable Models for Mixed Discrete and Continuous Outcomes , 1997 .

[5]  E. T. Goodwin The evaluation of integrals of the form , 1949, Mathematical Proceedings of the Cambridge Philosophical Society.

[6]  Stan Lipovetsky,et al.  Latent Variable Models and Factor Analysis , 2001, Technometrics.

[7]  David J. Hand,et al.  Graphical models of applicants for credit , 1997 .

[8]  Niall M. Adams,et al.  Supervised classification with structured class definitions , 2001 .

[9]  A. Goldberger,et al.  Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable , 1975 .

[10]  Elizabeth Mays,et al.  Handbook of Credit Scoring , 2001 .

[11]  Irene A. Stegun,et al.  Handbook of Mathematical Functions. , 1966 .

[12]  D. Hand Modelling consumer credit risk , 2001 .

[13]  E. Crouch,et al.  The Evaluation of Integrals of the form ∫+∞ −∞ f(t)exp(−t 2) dt: Application to Logistic-Normal Models , 1990 .

[14]  R. P. McDonald,et al.  Structural Equations with Latent Variables , 1989 .

[15]  Sophia Rabe-Hesketh,et al.  Latent variable modelling , 2007 .

[16]  William H. Press,et al.  Numerical recipes in C , 2002 .

[17]  Eric Rosenberg,et al.  Quantitative Methods in Credit Management: A Survey , 1994, Oper. Res..

[18]  Joe Whittaker,et al.  Graphical models in credit scoring. , 1998 .

[19]  Jonathan Crook,et al.  Sample selection bias in credit scoring models , 2003, J. Oper. Res. Soc..

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

[21]  David J. Hand,et al.  Can reject inference ever work , 1993 .

[22]  L. Thomas A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers , 2000 .