Adaptive Risk Scoring Models in FinTech Leveraging Artificial Intelligence and Privacy-Preserving Encryption

Abstract

The rapid growth of financial technology (FinTech) platforms has intensified the need for dynamic, accurate, and secure risk assessment systems. Traditional static risk scoring models are insufficient to address evolving fraud patterns, behavioral anomalies, and sophisticated cyber threats. This paper proposes an adaptive risk scoring framework that integrates artificial intelligence (AI) with privacy-preserving encryption techniques. The model continuously updates risk scores using behavioral analytics, transaction patterns, and contextual intelligence while ensuring that sensitive financial data remains protected through advanced cryptographic mechanisms such as homomorphic encryption and differential privacy. Experimental results demonstrate that adaptive AI-driven risk scoring significantly improves fraud detection accuracy and reduces false positives while maintaining compliance with data protection regulations. The proposed architecture offers a scalable, secure, and intelligent approach to modern financial risk management.