Credit Scoring and Data Mining

Credit scoring is the use of predictive modelling techniques to support decision making in lending. It is a field of immense practical value that also supports a modest amount of academic research. Interestingly, the academic research tends not to be put into practice. This is not a result of insularity and arrogance on the part of the practitioners, but rather, of the practitioners having a better understanding of where they add value. This arises because credit scoring (and probably many other analytical applications) is dominated by shallow pragmatic issues rather than deep theoretical issues. In this talk I give examples of practical issues in credit scoring. Copyright c ©2009, Australian Computer Society, Inc. This paper appeared at the Eighth Australasian Data Mining Conference (AusDM 2009), Melbourne, Australia. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 101, Paul J. Kennedy, Kok-Leong Ong and Peter Christen, Ed. Reproduction for academic, not-for profit purposes permitted provided this text is included. Proc. of the 8th Australasian Data Mining Conference (AusDM'09)