Multiple classifier architectures and their application to credit risk assessment

Multiple classifier systems combine several individual classifiers to deliver a final classification decision. In this paper the performance of several multiple classifier systems are evaluated in terms of their ability to correctly classify consumers as good or bad credit risks. Empirical results suggest that some multiple classifier systems deliver significantly better performance than the single best classifier, but many do not. Overall, bagging and boosting outperform other multi-classifier systems, and a new boosting algorithm, Error Trimmed Boosting, outperforms bagging and AdaBoost by a significant margin.

[1]  Rong Yang,et al.  Machine Learning and Data Mining in Pattern Recognition , 2012, Lecture Notes in Computer Science.

[2]  Michael S. Long.  CREDIT SCREENING SYSTEM SELECTION , 1976 .

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

[4]  H Zhu,et al.  A Bayesian framework for the combination of classifier outputs , 2002, J. Oper. Res. Soc..

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

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

[7]  Robert E. Schapire,et al.  The strength of weak learnability , 1990, Mach. Learn..

[8]  Bart Baesens,et al.  A Comprehensible SOM-Based Scoring System , 2005, MLDM.

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

[10]  Helen McNab,et al.  Consumer Credit Risk Management , 2008 .

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

[12]  Yachen Lin,et al.  Improvement on Behavior Scores by Dual-Model Scoring System , 2002, Int. J. Inf. Technol. Decis. Mak..

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

[14]  Gary M. Weiss.  Mining with rarity: a unifying framework , 2004, SKDD.

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

[16]  David West,et al.  Neural network ensemble strategies for financial decision applications , 2005, Comput. Oper. Res..

[17]  James C. Bezdek,et al.  Decision templates for multiple classifier fusion: an experimental comparison , 2001, Pattern Recognit..

[18]  L C Thomas,et al.  Recalibrating scorecards , 2001, J. Oper. Res. Soc..

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

[20]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[21]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[22]  Adam Weintrit,et al.  Methods and Algorithms , 2011 .

[23]  L. Thomas.  Consumer credit models: pricing, profit and portfolios , 2009 .

[24]  D. Ruppert,et al.  Trimmed Least Squares Estimation in the Linear Model , 1980 .

[25]  David West,et al.  Neural network credit scoring models , 2000, Comput. Oper. Res..

[26]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

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

[28]  Reserve Bank,et al.  Financial Stability Review , 2011 .

[29]  Jonathan Crook,et al.  Scoring by usage , 2001, J. Oper. Res. Soc..

[30]  D. Durand.  Risk elements in consumer instalment financing , 1942 .

[31]  Yoav Freund,et al.  An Adaptive Version of the Boost by Majority Algorithm , 1999, COLT '99.

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

[33]  Yoav Freund,et al.  An Adaptive Version of the Boost by Majority Algorithm , 1999, COLT.

[34]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[35]  D. Hand,et al.  A k-nearest-neighbour classifier for assessing consumer credit risk , 1996 .

[36]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[37]  David J. Hand,et al.  Classifier Technology and the Illusion of Progress , 2006, math/0606441.

[38]  William Edward Henley,et al.  Statistical aspects of credit scoring , 1995 .

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

[40]  Gianluca Antonini,et al.  Subagging for credit scoring models , 2010, Eur. J. Oper. Res..

[41]  Naeem Siddiqi,et al.  Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring , 2005 .

[42]  Bart Baesens,et al.  Comprehensible Credit Scoring Models Using Rule Extraction from Support Vector Machines , 2007, Eur. J. Oper. Res..

[43]  L. Thomas,et al.  Does Scoring a Subpopulation Make a Difference , 1996 .

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

[45]  Ludmila I. Kuncheva,et al.  Switching between selection and fusion in combining classifiers: an experiment , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[46]  Joe Whittaker,et al.  The neglog transformation and quantile regression for the analysis of a large credit scoring database , 2005 .

[47]  Thomas G. Dietterich.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[48]  A. L. Kroeber,et al.  Statistical Classification , 1940, American Antiquity.

[49]  James H. Myers,et al.  The Development of Numerical Credit Evaluation Systems , 1963 .

[50]  Baozong Yuan,et al.  Multiple classifiers combination by clustering and selection , 2001, Inf. Fusion.

[51]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[52]  Josef Kittler,et al.  Combining classifiers: A theoretical framework , 1998, Pattern Analysis and Applications.

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

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

[55]  Vijay S. Desai,et al.  A comparison of neural networks and linear scoring models in the credit union environment , 1996 .

[56]  Adnan Acan,et al.  Multiple classifier implementation of a divide-and-conquer approach using appearance-based statistical methods for face recognition , 2004, Pattern Recognit. Lett..

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

[58]  Peter A. Beling,et al.  A study in the combination of two consumer credit scores , 2001, J. Oper. Res. Soc..