An experimental investigation of artificial immune system algorithms for credit risk assessment applications

In the past few years, artificial immune systems (AIS) have been proposed as an alternative approach to solve computational intelligence problems. Despite their interesting properties and theoretical appeal, the AIS algorithms still require a broader experimentation in real and large scale problems to ensure that they can approximate or outperform the more traditional techniques. In this work, the two known AIS models SAIS and CLONALG are studied in the problem of credit risk analysis making use of three benchmarking databases for comparative analysis. Further variations of the initially proposed algorithms are experimented and their performance are compared to classic logistic regression with respect to the KS (kolmogorov-Smirnov) test and ROC curves. Also, a method for score computation is introduced for allowing a more robust analysis through the ROC and KS metrics. The results can shed some light into the potential of using AIS models in daily real-world credit risk assessment operations.