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With the success of big data and artificial intelligence in many fields, theapplications of big data driven models are expected in financial riskmanagement especially credit scoring and rating. Under the premise of dataprivacy protection, we propose a projected gradient-based method in thevertical federated learning framework for the traditional scorecard, which isbased on logistic regression with bounded constraints, namely FL-LRBC. Thelatter enables multiple agencies to jointly train an optimized scorecard modelin a single training session. It leads to the formation of the model withpositive coefficients, while the time-consuming parameter-tuning process can beavoided. Moreover, the performance in terms of both AUC and theKolmogorov-Smirnov (KS) statistics is significantly improved due to dataenrichment using FL-LRBC. At present, FL-LRBC has already been applied tocredit business in a China nation-wide financial holdings group.

A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring