FedIncome: Federated Learning for Income Estimation in Digital Lending Under Data Sovereignty Constraints

Paper: arXiv 2609.27654

Authors: Sultan Amed, Tanmay Sen, Sayantan Banerjee

Abstract

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-time $R^2=0.608$, compared with $0.619$ for a pooled centralised benchmark. Small-sample clients obtain an average out-of-time $R^2$ improvement of $3.8$ percentage points relative to the pooled centralised benchmark, while the fitted client-level relationship places the empirical crossover at approximately $4,790$ training observations in this setting. When pooling is infeasible and the relevant alternative is local-only training, federation improves out-of-time performance across all sample-size groups, with the largest gains for data-scarce clients. We also combine federated income estimates with state- and income-specific debt-to-income thresholds. In a retrospective decision analysis, replacing reported income with the federated estimate increases simulated approval rates with only modest changes in observed default rates. FedIncome supports collaborative learning under data-locality constraints with little aggregate loss relative to pooled training and larger gains relative to local-only estimation.

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper presents a robust empirical study of federated learning for income estimation, a novel application in digital lending. The methodology is clearly explained, and the use of a large, partitioned dataset for simulation enhances its empirical rigor. While the mathematical derivations are not explicitly detailed in the summary, the underlying federated learning framework and statistical analysis suggest a solid mathematical foundation.

Research Flowchart

  flowchart TD
    A[Research Goal: Estimate Income in Digital Lending Under Data Sovereignty Constraints] --> B{Key Methodology: Federated Learning (FedIncome) Framework};
    B --> C[Data/Inputs: LendingClub Loans (1M+), Partitioned into 50 State-Level Clients];
    C --> D{Computational Process: Train Shared Model without Pooling Raw Data, Simulate Heterogeneous Consortium};
    D --> E[Key Finding 1: Best FedIncome model achieves Out-of-Time R^2 = 0.608 (vs. 0.619 for pooled benchmark)];
    E --> F[Key Finding 2: Small-sample clients gain 3.8 ppt R^2 improvement; Crossover at ~4,790 training observations];
    F --> G[Key Finding 3: Federation increases simulated approval rates with modest changes in default rates (using FedIncome estimates + DTI thresholds)];

Cited References (73)

1. Privacy-preserving federated credit risk models: evaluating differential privacy and homomorphic encryption techniques (2026)
2. FSL-BDP: Federated Survival Learning with Bayesian Differential Privacy for Credit Risk Modeling — → 2601.11134
3. Ability-to-Repay and Qualified Mortgage Standards Under the Truth in Lending Act (Regulation Z) (2013) — link
4. The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform (2019) — link
5. Fintech and big tech credit: Drivers of the growth of digital lending (2023) — link
6. A privacy-preserving decentralized credit scoring method based on multi-party information (2023) — link
7. Directive 2014/17/EU on credit agreements for consumers relating to residential immovable property (2014) — link
8. Regulation ( EU ) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/ EC ( General Data Protection Regulation ) (2016) — link

1 of 73 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.