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Predicting potential credit default accounts in advance is challenging.Traditional statistical techniques typically cannot handle large amounts ofdata and the dynamic nature of fraud and humans. To tackle this problem, recentresearch has focused on artificial and computational intelligence basedapproaches. In this work, we present and validate a heuristic approach to minepotential default accounts in advance where a risk probability is precomputedfrom all previous data and the risk probability for recent transactions arecomputed as soon they happen. Beside our heuristic approach, we also apply arecently proposed machine learning approach that has not been appliedpreviously on our targeted dataset [15]. As a result, we find that theseapplied approaches outperform existing state-of-the-art approaches.

Credit Default Mining Using Combined Machine Learning and Heuristic Approach