Privacy-Preserving Risk Prediction and Sensitive Data Detection in FinTech Platforms: A Hybrid Approach for Secure and Intelligent Early Warning

Abstract

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.