AI-Driven Credit Risk and Decision Intelligence in Digital Fintech Ecosystems

The rapid expansion of digital fintech ecosystems has transformed credit provision, risk evaluation, and financial decision-making. While artificial intelligence (AI) and machine learning (ML) techniques have substantially enhanced predictive accuracy in credit risk assessment, their integration into decision intelligence frameworks remains fragmented, opaque, and often misaligned with regulatory and governance requirements. This study proposes an AI-driven credit risk and decision intelligence framework tailored for digital fintech ecosystems, integrating predictive modeling, Explainability mechanisms, and decision-support analytics. Using a synthetic but policy-realistic fintech dataset reflecting digital lending platforms, transaction histories, and customer behavioral signals, multiple supervised learning models are evaluated for probability of default (PD), loss given default (LGD), and expected loss (EL) estimation. Explainable AI (XAI) techniques are applied to interpret model behavior, detect bias, and assess stability under changing economic conditions. The proposed framework demonstrates that decision-oriented integration of AI models improves risk transparency, governance readiness, and operational usability without materially degrading predictive performance. The study contributes to fintech literature by bridging high-performance AI modeling with interpretable, regulator-aware decision intelligence systems suitable for modern digital finance environments.