The rapid expansion of FinTech platforms has elevated the urgency of balancing predictive risk intelligence with stringent privacy and regulatory constraints. This paper proposes a hybrid, privacy-preserving early warning system that integrates sensitive information detection, federated learning (FL), and differential privacy (DP) to address the unique challenges of secure data analytics in financial systems. We construct a comprehensive risk modeling pipeline that detects sensitive entities using transformer-based natural language processing, applies risk scoring via privacy-compliant federated learning, and generates cryptographically auditable alerts. A hybrid synthetic dataset simulating financial transactions, session metadata, and communication logs was used to benchmark performance under GDPR-aligned conditions. The model maintains high F1-scores (>0.85) even under strong DP noise, with real-time alert latency averaging 187 ms. A regulatory-aligned sensitivity labeling taxonomy and feedback-driven alert refinement further ensure interpretability and compliance. Extensive evaluation highlights the feasibility of deploying real-time, privacy-preserving predictive systems in FinTech environments without compromising utility. Our findings support the broader adoption of integrated, regulation-aware security architectures for scalable and responsible FinTech innovation.
Ye Ju (Tue,) studied this question.