Machine Learning for Systemic Risk Prediction in FinTech Lending: A Cross-Country Analysis Using Public Data

Abstract

FinTech lending has grown rapidly to nearly $800 billion globally, yet systemic risk assessment within these markets remains largely understudied. Most existing research focuses on individual loan defaults rather than system-wide stability threats. This paper develops and validates a methodological framework for predicting systemic risk in FinTech lending markets using exclusively public data sources. The research demonstrates the approach through a comparative analysis of gradient boosting ensemble methods (XGBoost, LightGBM)  against traditional early warning indicators across developed and emerging markets. Using the BIS FinTech Credit Database, IMF Financial Access Survey data (2004-2023), and platform-level information from regulatory sources, we construct a FinTech Systemic Stress Index and test the predictive accuracy of machine learning models. XGBoost achieves AUC scores of 0.82-0.87 in developed markets but shows 15-23 percentage point accuracy degradation when applied to emerging markets without retraining. Network centrality measures and funding concentration ratios emerge as the strongest predictors of systemic stress, explaining 45-62% of variance. Models trained on developed market data require substantial feature reweighting to transfer effectively to developing economies, where alternative data sources and regulatory regime indicators become critical. The methodology enables rigorous systemic risk analysis without requiring proprietary platform partnerships, offering particular value for regulators and researchers in data-restrictive environments. We provide replication code and detailed guidance for applying the framework across different market contexts.