Methodology for Smooth Transition from Experience-Based to Data-Driven Credit Risk Assessment Modeling under Data Scarcity

Hengchun Li, Qiujun Lan and Qingyue Xiong ()
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Hengchun Li: School of Economics and Management, Shaoyang University, Shaoyang 422000, China
Qiujun Lan: Hunan Key Laboratory of Data Science & Blockchain, Business School of Hunan University, Changsha 410082, China
Qingyue Xiong: Hunan Key Laboratory of Data Science & Blockchain, Business School of Hunan University, Changsha 410082, China

Mathematics, 2024, vol. 12, issue 15, 1-22

Abstract: Credit risk refers to the possibility of borrower default, and its assessment is crucial for maintaining financial stability. However, the journey of credit risk data generation is often gradual, and machine learning techniques may not be readily applicable for crafting evaluations at the initial stage of the data accumulation process. This article proposes a credit risk modeling methodology, TED-NN, that first constructs an indicator system based on expert experience, assigns initial weights to the indicator system using the Analytic Hierarchy Process, and then constructs a neural network model based on the indicator system to achieve a smooth transition from an empirical model to a data-driven model. TED-NN can automatically adapt to the gradual accumulation of data, which effectively solves the problem of risk modeling and the smooth transition from no to sufficient data. The effectiveness of this methodology is validated through a specific case of credit risk assessment. Experimental results on a real-world dataset demonstrate that, in the absence of data, the performance of TED-NN is equivalent to the AHP and better than untrained neural networks. As the amount of data increases, TED-NN gradually improves and then surpasses the AHP. When there are sufficient data, its performance approaches that of a fully data-driven neural network model.

Keywords: credit risk assessment; neural networks; machine learning; data scarcity; analytic hierarchy process (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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