“…Another popular alternative is to use classification trees, with its origins both in statistics ( Breiman 1984) and machine learning (Quinlan 1993), though of course this ends up not with a scorecard but with groups of customers described by combinations of their characteristics where each group is classified as either Good or Bad . However any classification approach can be applied to the credit scoring problem and so in the past twenty years researchers have tried neural nets ( Desai et al 1997, Malhotra andMalhotra 2002), support vector machines ( Huang et al 2007, van Gestel et al 2003, Bellotti and Crook 2009a , genetic algorithms ( Desai et al 1997,Ong et al 2005, nearest neighbour methods (Chatterjee and Barcun (1970), Henley and Hand (1996)) and ant colony optimization ( Martens et al 2007). So what methodology gives a scorecard with the best discrimination in credit scoring?…”