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A federated interpretable scorecard and its application in credit scoring

Author

Listed:
  • Fanglan Zheng

    (Everbright Technology Co. Ltd, Beijing 100040, P. R. China)

  • Erihe

    (Everbright Technology Co. Ltd, Beijing 100040, P. R. China)

  • Kun Li

    (Everbright Technology Co. Ltd, Beijing 100040, P. R. China)

  • Jiang Tian

    (Everbright Technology Co. Ltd, Beijing 100040, P. R. China)

  • Xiaojia Xiang

    (Everbright Technology Co. Ltd, Beijing 100040, P. R. China)

Abstract

In this paper, we propose a vertical federated learning (VFL) structure for logistic regression with bounded constraint for the traditional scorecard, namely FL-LRBC. Under the premise of data privacy protection, FL-LRBC enables multiple agencies to jointly obtain an optimized scorecard model in a single training session. It leads to the formation of scorecard model with positive coefficients to guarantee its desirable characteristics (e.g., interpretability and robustness), while the time-consuming parameter-tuning process can be avoided. Moreover, model performance in terms of both AUC and the Kolmogorov–Smirnov (KS) statistics is significantly improved by FL-LRBC, due to the feature enrichment in our algorithm architecture. Currently, FL-LRBC has already been applied to credit business in a China nation-wide financial holdings group.

Suggested Citation

  • Fanglan Zheng & Erihe & Kun Li & Jiang Tian & Xiaojia Xiang, 2021. "A federated interpretable scorecard and its application in credit scoring," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 8(03), pages 1-14, September.
    • Handle: RePEc:wsi:ijfexx:v:08:y:2021:i:03:n:s2424786321420093
    DOI: 10.1142/S2424786321420093
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