A federated interpretable scorecard and its application in credit scoring

Fanglan Zheng (), Erihe (), Kun Li, Jiang Tian () and Xiaojia Xiang ()
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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

International Journal of Financial Engineering (IJFE), 2021, vol. 08, issue 03, 1-14

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.

Keywords: Scorecard; logistic regression; bounded constraints; federated learning; financial holdings group (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S2424786321420093

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