Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence

M. K. Nallakaruppan, Himakshi Chaturvedi, Veena Grover, Balamurugan Balusamy, Praveen Jaraut, Jitendra Bahadur, V. P. Meena () and Ibrahim A. Hameed ()
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M. K. Nallakaruppan: Balaji Institute of Modern Management, Sri Balaji University, Pune 411033, India
Himakshi Chaturvedi: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632104, India
Veena Grover: Department of Management, Noida Institute of Engineering and Technology, Noida 201310, India
Balamurugan Balusamy: Associate Dean-Student Affiars, Shiv Nadar University, Noida 201314, India
Praveen Jaraut: Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India
Jitendra Bahadur: Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India
V. P. Meena: Department of Electrical Engineering, National Institute of Technology Jamshedpur, Jamshedpur 831014, India
Ibrahim A. Hameed: Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Larsgardsvegen, 2, 6009 Alesund, Norway

Risks, 2024, vol. 12, issue 10, 1-18

Abstract: The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are more accessible than ever, but it has been challenging to create and implement systems that support real-world financial applications, primarily due to their lack of transparency and explainability—both of which are essential for building trustworthy technology. The novelty of this study lies in the development of an explainable AI (XAI) model that not only addresses these transparency concerns but also serves as a tool for policy development in credit risk management. By offering a clear understanding of the underlying factors influencing AI predictions, the proposed model can assist regulators and financial institutions in shaping data-driven policies, ensuring fairness, and enhancing trust. This study proposes an explainable AI model for credit risk management, specifically aimed at quantifying the risks associated with credit borrowing through peer-to-peer lending platforms. The model leverages Shapley values to generate AI predictions based on key explanatory variables. The decision tree and random forest models achieved the highest accuracy levels of 0.89 and 0.93, respectively. The model’s performance was further tested using a larger dataset, where it maintained stable accuracy levels, with the decision tree and random forest models reaching accuracies of 0.90 and 0.93, respectively. To ensure reliable explainable AI (XAI) modeling, these models were chosen due to the binary classification nature of the problem. LIME and SHAP were employed to present the XAI models as both local and global surrogates.

Keywords: RMSE; AI; XAI; random forest; decision tree (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2024
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