I Evaluated Half a Million Credit Records with Federated Learning. Here’s What I Found

Credit scoring models face a difficult trade-off between privacy, fairness, and accuracy when trained on a single institution's data. Implementing differential privacy adds statistical noise, which can prevent fairness algorithms from effectively detecting and correcting demographic bias. This tension can be resolved through federated learning, which allows multiple institutions to collaboratively train a shared model without exposing private data. By aggregating learning from 300 institutions and 500,000 records, the federated approach simultaneously achieves high accuracy (96.9%), exceptional fairness (0.069% gap), and strong privacy guarantees, resolving the paradox faced by smaller-scale systems.