Abstract
The rapid growth of taking loans and digitizing the financial sector is increasing the rate of loan charge-offs as well as the volume of data that represents customer behavior. Nowadays, Machine Learning (ML) technology is helping financial institutions utilize this huge amount of data and build some black-box prediction models for predicting loan charge-offs with decent accuracy. Yet, the amount of risk involved in such financial decisions is very high and should not be taken only based on an opaque decision of a black-box model. In this study, we propose a system for building accurate models using interpretable state-of-the-art (SOTA) ML algorithms as well as utilizing the Explainable AI (XAI) techniques to explain individual instances for supporting business decisions.
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Imran, A.A., Amin, M.N. (2021). Loan Charge-Off Prediction Including Model Explanation for Supporting Business Decisions. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_119
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DOI: https://doi.org/10.1007/978-3-030-71187-0_119
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