AI-Powered Fraud Detection: Strengthening Risk Monitoring with Business Intelligence in U.S. Financial Institutions

Abstract

The growing complexity of financial fraud in the United States has pushed organizations to adopt advanced technologies for more effective risk monitoring. This study examines how various U.S. financial institutions—including banks, fintech firms, and credit unions—implement AI and business intelligence (BI) tools for fraud detection. A survey of 400 professionals from these sectors investigates how AI adoption relates to trust in the technology, staff training levels, BI usage, and future investment intentions. In addition to standard statistical analyses, machine learning models were applied to uncover hidden patterns influencing adoption behavior. The results indicate that AI integration is driven mainly by investment readiness, confidence in AI, the extent of BI utilization, and perceived AI speed, whereas individual perceptual factors alone show limited significance. Overall, the findings suggest that successful AI adoption is shaped by organizational strategy, institutional culture, and existing technological infrastructure. To maximize the effectiveness of fraud detection, U.S. financial institutions should adopt integrated AI–BI solutions, maintain regulatory compliance, and enhance workforce skills to fully leverage the capabilities of AI.