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:
Due to a rise in data complexity and strict regulations, small financial firms in the US are now required to update their systems and procedures. It looks into the influence of combining Artificial Intelligence (AI) and Business Intelligence (BI) on productivity, accurate reporting, and confident decision-making in U.S.-based small and mid-sized financial firms. A cross-sectional survey was conducted to collect information from 400 financial professionals in various roles across companies of different sizes. According to the results, firms that work with AI and BI have better productivity and fewer mistakes in reporting than companies that do not use these technologies. The regression analysis proved that BI and AI played major roles in predicting performance, and the correlation analysis found a positive relationship between reporting and decision confidence (r = 0.64). The results of Exploratory Factor Analysis validated the model and indicated two distinct constructs: productivity and reporting accuracy, with a cumulative variance explained of 74.1%. There are still obstacles, like a lack of technical knowledge and integration issues, that many people face. The results provide useful directions for leaders, policymakers, and technology providers who wish to enhance the efficiency and regulatory compliance of small banks using AI and BI.
Keywords: Artificial Intelligence (AI); Business Intelligence (BI); Reporting Accuracy; Financial SMEs; Digital Transformation; U.S. Financial Sector; Decision-Making; Regulatory Compliance; Financial Firms.
Received: 26/04/2024, Revised: 20/06/2024, Accepted: 21/08/2024, Published: 05/06/2025
DOI: 10.64091/ATIML.2025.000136
AVE Trends in Intelligent Management Letters, 2025 Vol. 1 No. 2 , Pages: 59-75