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

Understand

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.