Explainable Machine Learning Models for Fraud Prevention and Secure Data Governance in FinTech

Abstract

As FinTech platforms handle increasing volumes of sensitive financial transactions, ensuring fraud prevention while maintaining transparent and accountable AI systems is critical. Traditional black-box machine learning models, although effective at anomaly detection, lack interpretability, limiting trust and regulatory compliance. This paper proposes an integrated framework of explainable machine learning models for fraud prevention combined with secure data governance mechanisms. The approach leverages interpretable models (e.g., SHAP, LIME, and attention-based neural networks) to provide transparency in decision-making, while advanced encryption and access controls ensure secure data handling. Experiments demonstrate that explainable AI improves stakeholder trust, supports regulatory compliance, and maintains high fraud detection performance without compromising data security. The framework establishes a foundation for responsible, secure, and interpretable AI adoption in modern FinTech ecosystems.