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Incorporating Digital Footprints into Credit-Scoring Models through Model Averaging

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  • Linhui Wang

    (School of Management, Xiamen University, Xiamen 361005, China
    Data Mining Research Center, Xiamen University, Xiamen 361005, China)

  • Jianping Zhu

    (School of Management, Xiamen University, Xiamen 361005, China
    Data Mining Research Center, 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)

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.

Suggested Citation

  • Linhui Wang & Jianping Zhu & Chenlu Zheng & Zhiyuan Zhang, 2024. "Incorporating Digital Footprints into Credit-Scoring Models through Model Averaging," Mathematics, MDPI, vol. 12(18), pages 1-15, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2907-:d:1480491
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    References listed on IDEAS

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