Consumer Credit

Explainable Federated Learning for U.S. State-Level Financial Distress Modeling ArXiv ID: 2511.08588 “View on arXiv” Authors: Lorenzo Carta, Fernando Spadea, Oshani Seneviratne Abstract We present the first application of federated learning (FL) to the U.S. National Financial Capability Study, introducing an interpretable framework for predicting consumer financial distress across all 50 states and the District of Columbia without centralizing sensitive data. Our cross-silo FL setup treats each state as a distinct data silo, simulating real-world governance in nationwide financial systems. Unlike prior work, our approach integrates two complementary explainable AI techniques to identify both global (nationwide) and local (state-specific) predictors of financial hardship, such as contact from debt collection agencies. We develop a machine learning model specifically suited for highly categorical, imbalanced survey data. This work delivers a scalable, regulation-compliant blueprint for early warning systems in finance, demonstrating how FL can power socially responsible AI applications in consumer credit risk and financial inclusion. ...

The Age of Reason: Financial Decisions Over the Lifecycle ArXiv ID: ssrn-1293139 “View on arXiv” Authors: Unknown Abstract The sophistication of financial decisions varies with age: middle-aged adults borrow at lower interest rates and pay fewer fees compared to both younger and old Keywords: Household Debt, Interest Rates, Credit Markets, Life-Cycle Finance, Consumer Credit Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses standard econometric regression techniques to analyze large-scale financial datasets (mortgages, credit cards, etc.), which involves data processing and implementation, but the mathematical models are primarily descriptive statistics and linear regressions without heavy theoretical derivations. flowchart TD A["Research Goal:<br/>How does age influence<br/>sophistication of financial decisions?"] B["Methodology:<br/>Analysis of Household<br/>Credit Survey Data"] C["Data: Loan terms,<br/>interest rates, fees<br/>across age groups"] D["Computation:<br/>Regression & statistical<br/>comparison of outcomes"] E["Key Finding 1:<br/>Middle-aged adults<br/>secure lower interest rates"] F["Key Finding 2:<br/>Middle-aged adults<br/>pay fewer fees"] G["Conclusion:<br/>Financial decision<br/>sophistication peaks<br/>in middle age"] A --> B B --> C C --> D D --> E D --> F E --> G F --> G

The Age of Reason: Financial Decisions Over the Lifecycle ArXiv ID: ssrn-997547 “View on arXiv” Authors: Unknown Abstract In cross-sectional data sets from ten credit markets, we find that middle-aged adults borrow at lower interest rates and pay fewer fees relative to younger and Keywords: Credit Markets, Borrowing Costs, Cross-Sectional Analysis, Financial Intermediation, Consumer Credit Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper focuses on empirical analysis of cross-sectional credit market data with clear real-world applicability, but its mathematical depth appears limited to basic econometric models without advanced derivations. flowchart TD A["Research Goal:<br>Identify Lifecycle Patterns in<br>Borrowing Costs & Credit Access"] --> B["Data Source:<br>Cross-Sectional Credit Data<br>from 10 Markets"] B --> C["Key Methodology:<br>Cross-Sectional Analysis<br>Segmentation by Age Group"] C --> D{"Computational Process"} D --> E["Compare Interest Rates<br>& Fees: Young vs. Middle vs. Old"] E --> F["Statistical Testing &<br>Intermediation Assessment"] F --> G["Key Findings:<br>Middle-Aged Adults Obtain<br>Lower Rates & Fewer Fees<br>Optimal Financial Decisions at Mid-Life"]