Authors:
Md. Asif Hasan, Md. Tanvir Rahman Mazumder, Md. Caleb Motari, Md. Shahadat Hossain Shourov, Mrinmoy Sarkar

Addresses:
School of Business, Montclair State University, Montclair, New Jersey, United States of America. School of Information Technology, Washington University of Science and Technology (WUST), Alexandria, Virginia, United States of America. Department of Information Technology Management, Webster University, Webster Groves, Missouri, United States of America.

Abstract:

The increasing difficulty of financial fraud in the United States has led companies to use modern technology to monitor risks. This study analyses how different U.S. financial organisations adopt AI and BI technologies and utilise them for fraud detection. The survey of 400 people from banking, FinTech and credit unions looks at how adoption of AI is related to trust, level of training, usage of BI and future investment decisions. Along with statistical procedures, machine learning models helped us find unexpected patterns in what influences adoption. AI integration primarily depends on investment readiness, confidence in AI, the use of business intelligence, and the rate of AI adoption. At the same time, the relationships with individual perceptual factors are not significant. According to the findings, adopting AI depends on several factors, including an organisation’s strategy, its culture and the technology it relies on. U.S. banks and financial institutions need to utilise integrated AI-BI systems, comply with all relevant regulations, and equip their staff with additional skills to leverage AI to its full potential in detecting fraud.

Keywords: Artificial Intelligence; Fraud Detection; Business Intelligence; Risk Monitoring; U.S. Financial Institutions; AI Adoption; Machine Learning; Financial Crime Prevention; Regulatory Compliance.

Received on: 12/10/2024, Revised on: 04/01/2025, Accepted on: 25/02/2025, Published on: 07/06/2025

DOI: 10.69888/FTSTPL.2025.000438

FMDB Transactions on Sustainable Technoprise Letters, 2025 Vol. 3 No. 2, Pages: 73-89

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