Abstract
Financial institutions, especially the banking sector, have become one of the major pillars of any economy. Given the modern banking platforms, businesses can conduct their activities more smoothly and fast. However, the banking industry is not immune to criticism. Fraudulent individuals are evident in conducting terrorist financing, financial fraud, money laundering, etc. Non-performing loan, default loan, is the major concern of today’s policymakers. Therefore, figuring out a way to control this threat is of utmost importance. Considering this, in this study, we propose a Machine Learning (ML) and Explainable AI (XAI)-based methodology to predict the P2P Bank Load Default (BLD) network and explain the hidden stories of loan default from the customers’ behavior. We employ 10 well-known ML algorithms to predict the BLD from the secondary dataset and apply four XAI tools (SHAP, SHAPASH, ELI5, LIME) on the top performer ML algorithm. The result reveals that Random Forest (RF) outperforms all the algorithms, and it shows 98% accuracy, precision, recall, and F1-score. The XAI tools find the top features for the bank loan default and the contribution of the top features to the model’s performance in terms of predictions. This study can be a guideline for the bank to verify a customer before issuing the loan and can update their policy based on the explainability outputs.
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Acknowledgements
We owe a great debt of gratitude to the Multidisciplinary Research Lab (MR Lab) and its members for their tireless assistance and insightful feedback during this study. Our study and our approach to problems were greatly aided by the advice and guidance we received.
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Islam, M.M., Sohag, A., Hasan, M., Islam, M.K., Sultan, M.N. (2024). XAI-Driven Model Explainability and Prediction of P2P Bank Loan Default Network. In: Arefin, M.S., Kaiser, M.S., Bhuiyan, T., Dey, N., Mahmud, M. (eds) Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning. BIM 2023. Lecture Notes in Networks and Systems, vol 867. Springer, Singapore. https://doi.org/10.1007/978-981-99-8937-9_8
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DOI: https://doi.org/10.1007/978-981-99-8937-9_8
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