Transparent AI Governance for Financial Fraud Prevention and Credit Risk Control

Abstract

The adoption of artificial intelligence in financial fraud prevention has created new opportunities for risk control, but it has also raised concerns about transparency, accountability, and model governance. This article examines how explainable AI and governance-oriented model design can improve fraud prevention and credit risk management. The proposed framework links fraud risk assessment, credit scoring, and supply-chain finance monitoring through a shared governance architecture. It includes model documentation, feature attribution, validation procedures, bias monitoring, and human-in-the-loop review. By combining traditional financial expertise with machine learning-based anomaly detection, the framework improves the interpretability and reliability of financial risk decisions. The article emphasizes that AI models should not replace professional judgment but should provide structured evidence for auditors, credit officers, and compliance managers. The study contributes to the literature by presenting a governance-centered approach to AI-enabled financial risk control, especially for institutions facing regulatory pressure to justify automated decisions.