Artificial Intelligence and Predictive Machine Learning for Financial Fraud Detection, Cyber Risk Management, and Infrastructure Resilience in the U.S. Banking Industry

Abstract

The U.S. banking industry operates in a threat environment shaped by fraud convergence, cyber-enabled financial crime, third-party concentration, and rising operational interdependence across payment, cloud, and identity infrastructures. Traditional control architectures remain necessary, but static rules and siloed monitoring often react too slowly to adversaries that exploit speed, scale, and cross-channel coordination (Ngai et al., 2011; Abdallah et al., 2016). This paper develops a predictive analytics framework that links artificial intelligence, machine learning, and resilience-oriented governance to three objectives: earlier detection of financial fraud, adaptive management of cyber risk, and protection of banking operations. The study synthesizes approximately fifty academic and policy sources and grounds the framework in authentic public data released through 2024, including FBI Internet Crime Complaint Center statistics, FinCEN Bank Secrecy Act reporting data, OCC cyber-resilience guidance, FDIC risk analysis, and Federal Reserve supervisory material (Federal Bureau of Investigation [FBI], 2022, 2023, 2024a; FinCEN, 2023, 2024a; FDIC, 2024; OCC, 2024a). The paper argues that effective banking surveillance requires multimodal architectures combining transaction features, customer behavior, alert narratives, entity relationships, authentication telemetry, and external threat intelligence. It further shows that fraud prevention and cyber resilience should be treated as a unified problem because payment fraud, account takeover, business email compromise, ransomware, identity abuse, and third-party disruption increasingly share infrastructure, indicators, and institutional consequences. The proposed methodology integrates anomaly detection, graph analytics, gradient-boosted classification, natural language processing, and explainable governance dashboards within a risk-based operating model. Rather than claiming proprietary bank-level back-test results unavailable in public data, the paper contributes an implementation blueprint, evaluation logic, policy design for U.S. banks seeking to improve detection quality while preserving safety, fairness, and operational resilience.