论文标题
一种用于解释记分卡的垂直联合学习方法及其在信用评分中的应用
A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring
论文作者
论文摘要
随着大数据和人工智能在许多领域的成功，预计大数据驱动模型的应用在财务风险管理中，尤其是信用评分和评级。在数据隐私保护的前提下，我们在传统记分卡的垂直联合学习框架中提出了一种基于梯度的方法，该方法基于具有有界约束的逻辑回归，即fl-lrbc。后者使多个机构能够在一次培训课程中共同培训优化的记分卡模型。它导致具有正系数的模型形成，而可以避免耗时的参数调整过程。此外，由于使用FL-LRBC的数据富集，AUC和Kolmogorov-Smirnov（KS）统计的性能都显着提高。目前，FL-LRBC已经在一个全国性的金融控股集团中应用于信用业务。
With the success of big data and artificial intelligence in many fields, the applications of big data driven models are expected in financial risk management especially credit scoring and rating. Under the premise of data privacy protection, we propose a projected gradient-based method in the vertical federated learning framework for the traditional scorecard, which is based on logistic regression with bounded constraints, namely FL-LRBC. The latter enables multiple agencies to jointly train an optimized scorecard model in a single training session. It leads to the formation of the model with positive coefficients, while the time-consuming parameter-tuning process can be avoided. Moreover, the performance in terms of both AUC and the Kolmogorov-Smirnov (KS) statistics is significantly improved due to data enrichment using FL-LRBC. At present, FL-LRBC has already been applied to credit business in a China nation-wide financial holdings group.
