Credit Default Mining Using Combined Machine Learning and Heuristic Approach
Abstract
Predicting potential credit default accounts in advance is challenging. Traditional statistical techniques typically cannot handle large amounts of data and the dynamic nature of fraud and humans. To tackle this problem, recent research has focused on artificial and computational intelligence based approaches. In this work, we present and validate a heuristic approach to mine potential default accounts in advance where a risk probability is precomputed from all previous data and the risk probability for recent transactions are computed as soon they happen. Beside our heuristic approach, we also apply a recently proposed machine learning approach that has not been applied previously on our targeted dataset [15]. As a result, we find that these applied approaches outperform existing state-of-the-art approaches.
Cite
@article{arxiv.1807.01176,
title = {Credit Default Mining Using Combined Machine Learning and Heuristic Approach},
author = {Sheikh Rabiul Islam and William Eberle and Sheikh Khaled Ghafoor},
journal= {arXiv preprint arXiv:1807.01176},
year = {2018}
}
Comments
Accepted for ICDATA, 2018