A bank-account-information-based credit scoring method with Bayesian hierarchical modeling

Suguru Yamanaka and Rei Yamamoto
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Suguru Yamanaka: College of Science and Engineering, Aoyama Gakuin University, 5-10-1 Fuchinobe Chuo-ku, Sagamihara-shi, Kanagawa, Japan
Rei Yamamoto: ��Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-Ku, Yokohama-shi, Kanagawa, Japan

International Journal of Financial Engineering (IJFE), 2022, vol. 09, issue 01, 1-16

Abstract: Recent interest in financial technology (fintech) lending business has caused increasing challenges of credit scoring models using bank account activity information. Our work aims to develop a new credit scoring method based on bank account activity information. This method incorporates borrower firms’ segment-level heterogeneity, such as a segment of sales size and firm age. We employ Bayesian hierarchical modeling, which mitigates data sparsity issue due to data segmentation. We describe our modeling procedures, including data handling and variable selection. Empirical results show that our model outperforms the traditional logistic model for credit scoring in information criterion. Our model realizes advanced credit scoring based on bank account activity information in fintech lending businesses, taking segment-specific features into credit risk assessment.

Keywords: Credit risk; credit scoring; bank account activity information; Bayesian hierarchical model; fintech (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S2424786321500365

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