A Proposed Framework for AI-Driven Microcredit Risk Mitigation in Mobile-Based Financial Inclusion Platforms in Emerging Markets
Abstract
Microcredit is vital for financial inclusion in emerging markets but suffers from high default rates due to data scarcity, inadequate risk models, and weak oversight. Traditional credit scoring fails to capture dynamic risk profiles of mobile-first, unbanked borrowers. This study proposes a novel six-layer AI-driven framework integrating XGBoost-based ensemble learning with federated learning for real-time, privacy-preserving credit assessment using alternative mobile data. Simulations on synthetic datasets and pilot evidence demonstrate high predictive accuracy (AUC up to 0.95) and potential to reduce default rates by 25%. The framework extends conceptual design into an empirically validated model using behavioral data from 1,120 Egyptian borrowers. An AI-enhanced I-Score derived from mobile usage across social media, e-mail, e-games, and e-commerce platforms significantly improves credit risk prediction accuracy in emerging markets while preserving borrower privacy and enabling e-commerce participation.
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