Incorporating Digital Footprints into Credit-Scoring Models through Model Averaging

Linhui Wang, Jianping Zhu (), Chenlu Zheng () and Zhiyuan Zhang
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Linhui Wang: School of Management, Xiamen University, Xiamen 361005, China
Jianping Zhu: School of Management, Xiamen University, Xiamen 361005, China
Chenlu Zheng: Public Administration Department, Fujian Police College, Fuzhou 350007, China
Zhiyuan Zhang: Artificial Intelligence and Model Development Center, Technology Development Department, Xiamen International Bank, Xiamen 361001, China

Mathematics, 2024, vol. 12, issue 18, 1-15

Abstract: Digital footprints provide crucial insights into individuals’ behaviors and preferences. Their role in credit scoring is becoming increasingly significant. Therefore, it is crucial to combine digital footprint data with traditional data for personal credit scoring. This paper proposes a novel credit-scoring model. First, lasso-logistic regression is used to select key variables that significantly impact the prediction results. Then, digital footprint variables are categorized based on business understanding, and candidate models are constructed from various combinations of these groups. Finally, the optimal weight is selected by minimizing the Kullback–Leibler loss. Subsequently, the final prediction model is constructed. Empirical analysis validates the advantages and feasibility of the proposed method in variable selection, coefficient estimation, and predictive accuracy. Furthermore, the model-averaging method provides the weights for each candidate model, providing managerial implications to identify beneficial variable combinations for credit scoring.

Keywords: digital footprints; credit scoring; model averaging; Kullback–Leibler loss (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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