Intelligent Risk Management in Digital Finance Using AI for Fraud Detection, Settlement Prediction, and Credit Scoring

This paper has presented a comprehensive AI-driven framework for intelligent risk management in digital financial systems, addressing three interconnected challenges through a unified, modular architecture. The Fraud Detection Module leverages multimodal data fusion and stacked ensemble learning to identify fraudulent transactions with an AUC-ROC of 0.967, substantially reducing both missed detections and false alarms. The Settlement Failure Prediction Module employs gradient boosting to generate prospective risk scores that enable operators to intervene before high-risk transactions become irrevocable, achieving an F1-score of 0.865. The Credit Scoring Module transforms alternative behavioral and transactional data into equitable creditworthiness assessments, expanding financial access while reducing demographic disparity gaps by 41% through adversarial debiasing. The integrated framework consistently surpasses rule-based and single-model benchmarks across all three domains, demonstrating that the complementary nature of the constituent modules amplifies overall system performance. The modular design ensures that each component can be independently enhanced, scaled, or replaced as technology and regulatory requirements evolve, providing financial institutions with a durable and adaptable risk management asset. Future work will focus on streaming inference integration, Transformer-based temporal modeling, federated learning for privacy-preserving cross-institutional training, and comprehensive XAI reporting. Collectively, these enhancements will further strengthen the system's transparency, robustness, and operational scalability, positioning AI-driven risk management as an indispensable pillar of next-generation digital finance infrastructure.

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