Governance Gaps AI-Driven Fraud Detection: Machine Learning Strategies for Countering Generative Fraud in the U.S. Financial System

Abstract

With the fast development of the new generation of artificial intelligence (AI) technologies, fraud in the sphere of the financial industry has taken a new form and is a challenge to the detection systems and regulatory regimes. This paper explores how AI-guided fraud detection has weaknesses in governance and assesses machine learning to counter generative fraud in the U.S. banking sector. Since the generation models are capable of creating advanced synthetic identities, deepfake transactions and adaptive fraud patterns, the traditional rule-based monitoring systems and pre-existing oversight frameworks become more limited. Based on the publicly available Credit Card Fraud Detection dataset, this study creates and compares various machine learning models, such as the Logistic Regression, the Random Forest, the XGBoost, and the Neural Networks, in extreme class-imbalanced conditions. The synthetic pattern of fraud and adversarial perturbations are used to measure model robustness and resilience to match any emerging pattern of generative fraud risks. Given imbalance between fraudulent transactions, precision-recall measures, F1-score and Area under the Precision-Recall Curve (AUPRC) are used to perform the evaluation of performance. In addition to technical assessment, the paper analyzes the issues of transparency, explainability, and model accountability that are related to black-box AI systems. The results demonstrate a performance-governance tradeoff: whereas highly developed ensemble and deep learning models have high predictive accuracy, they cause interpretability and regulatory compliance issues. The article reveals key gaps in the governance of the model risk management, auditability, and regulatory compatibility in the financial oversight framework in the U.S. It suggests a unified system of explainable AI, adversarial robustness testing, and more stringent regulatory rules to increase institutional stability to generative fraud. The study adds to the crossroads of financial technology, AI governance, and the policy of cyber security by offering both empirical and policy-based suggestions.