Comparison of data classification methods for predictive ranking of banks exposed to risk of failure.

The difficulty of understanding a financial institution's risk of default has been highlighted by multiple recent episodes in both the U.S. and in Europe. This paper describes a study on the empirical comparison of classification techniques for predictive ranking of the 12 month risk of default in banks. This work compares the scoring capabilities of different predictive models. The models compared were induced from past levels of risk exposure observed in historic data. The ranking performance of the models is compared by assessing the highest risk cases, using the left-hand side of the model's ROC curves (i.e., curves representing true positive to false positive rates). Empirical comparisons were performed using FDIC call report data and a one-year-ahead ranking prediction schema. This comparison demonstrates that inductive machine learning techniques can be successfully applied for predictive ranking of default risk. Observed results indicate better performance by symbolic rule or decision tree based models than by traditional modeling techniques based on statistical algorithms.