A Proposed Framework for AI-Driven Microcredit Risk Mitiga-tion in Mobile-Based Financial Inclusion Platforms in Emerg-ing Markets

Microcredit is a critical instrument for financial inclusion in emerging markets, yet persistently high default rates (5–8%) threaten sectoral sustainability, particularly among thin-file borrowers lacking formal credit histories. This study proposes the AI-Driven Microcredit Risk Mitigation Framework (ADMRMF), a six-layer Design Science Research artefact integrating federated XGBoost credit scoring with differential-privacy guarantees (ε = 1.0) and e-commerce onboarding. The framework formalises the Trust–Privacy–Inclusion Trade-Off and the Federated Behavioural Signalling Principle within a stochastic optimal-control model and provides a predictive (correlational) validation of its signalling core on a repeated cross-section of 1,120 Egyptian borrowers across four governorates. Behavioural features—social media usage, e-mail activity, e-games interaction, and e-commerce transactions—were used to generate an AI-enhanced I-Score aligned with national infrastructure. The federated XGBoost module achieved an AUC of 0.910 (95% CI 0.870–0.950), statistically indistinguishable from a centralised baseline (AUC = 0.909) and exceeding a logistic behavioural-only benchmark (AUC = 0.839). Joint-liability group borrowers exhibited lower observed default rates (16.2%) than solo borrowers (27.0%). These findings indicate that mobile behavioural signals are informationally sufficient for privacy-preserving credit scoring among thin-file borrowers; structural estimation of the underlying trust-dynamics model remains a task for future longitudinal research.