FedIncome: Federated Learning for Income Estimation in Digital Lending Under Data Sovereignty Constraints
Verified income is often unavailable in digital loan applications, forcing lenders to rely on reported income and potentially leading to over-lending, overly conservative offers, or rejection of creditworthy applicants. Cross-institutional data-sharing constraints make this problem especially difficult for smaller lenders with limited training data. We introduce FedIncome, a federated learning framework for income estimation that enables institutions to train a shared model without pooling raw borrower records. Using more than one million LendingClub loans partitioned into $50$ state-level clients, we simulate a heterogeneous lending consortium. The best federated model achieves out-of-ti...
| Authors | Sultan Amed, Tanmay Sen, Sayantan Banerjee |
|---|---|
| Primary category | stat.ML |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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