Credit risk analysis using a hybrid data mining model

Abstract

Credit risk analysis is an important topic in financial risk management. Owing to recent financial crises, credit risk analysis has been the major focus of the financial and banking industry. An accurate estimation of credit risk could be transformed into a more efficient use of economic capital. To this end, a number of experiments have been conducted using representative learning algorithms, which were tested using two publicly credit datasets. The decision of which particular method to choose is a complicated problem. A good alternative to choosing only one method is to create a hybrid forecasting system incorporating a number of possible solution methods as components (an ensemble of classifiers). For this purpose, we have implemented a hybrid decision support system that combines the representative algorithms using a selective voting methodology and achieves better performance than any examined simple and ensemble method.