Transforming Business Analytics: The Impact of Machine Learning on Performance Prediction in US financial sectors

Abstract

This paper explores the disruptive nature of Machine Learning (ML) within the domain of performance prediction within the U.S. financial industry to understand the main factors that impact or restrict successful adoption of ML. It explores the nature, of awareness, implementation, challenges, and demographics in determining the effectiveness of ML in business analytics. The study used a quantitative research pattern, and a structured questionnaire was availed to 350 professional individuals who arched into the fields of finance, retail, technology, healthcare, and manufacturing. The 5-point Likert scale was used in measuring the four dimensions which are awareness, implementation, impact as well as challenges. The analysis of data was achieved by use of descriptive statistics, reliability (Cronbach alpha=0.87), inferential tests (t-tests, ANOVA), and multiple regression to determine some predictive relationship. The findings showed that ML awareness level was high (mean >4.0), and the performance prediction enhanced strongly especially on the areas of forecasting accuracy (mean=4.18) and strategic planning (mean=4.25). Nonetheless, training support by the organization (mean=3.90) as well as data quality (mean=3.80) were identified to be a gap. The influence of ML was more positive according to the technical workers (p<0.001) than jobs in other fields, and the technology/IT industry was ahead of others in terms of maturity in adoption. Successful Predictors The predictors of success were implementation (0.426) and awareness (0.354), whereas preventing impediments such as shortage of skills (mean = 4.20) and lacking interpretability (mean=4.10) proved challenging. This paper would help with a sector-specific examination of ML in financial analytics that would help create a linkage between theoretical capabilities and implementation strategies. It also combines demographic and organizational variables in an exclusive way to suggest specific approaches to solving the adoption barriers. The results contribute to the discussion about explainable AI and data governance, and they provide practical recommendations to financial institutions to benefit from ML in avoiding to cause ethical and operational risks.

References

Afshar, M. Z., & Shah, D. M. H. (2025). Strategic evaluation using PESTLE and SWOT frameworks: Public sector perspective. ISRG Journal of Economics, Business & Management (ISRGJEBM), 3(1), 108-114.

Afshar, M. Z., & Shah, M. H. (2025). Leveraging Porter's Diamond Model: Public Sector Insights. The Critical Review of Social Sciences Studies, 3(2), 2255-2271.

Ahmad, S., & Museera, S. (2024). The Strategic Influence of Cloud Computing on Contemporary Marketing and Management Practices. Journal of Engineering and Computational Intelligence Review, 2(2), 21-30.

Ahmad, S., Nisa, G., Shah, S. H. I., & Manzoor, H. (2025). Exploring the Interplay of Creativity and Proactive Personality: The Mediating Role of Self-Efficacy in Entrepreneurial Behavior. Research Journal for Social Affairs, 3(4), 233-242.

Alim, I. (2025). The impact of artificial intelligence on the accounting profession: Technological advancements and employment perspectives. International Journal of Science and Research Archive, 15(3), 1173-1187. https://doi.org/10.30574/ijsra.2025.15.3.1873

Appelbaum, D., Kogan, A., Vasarhelyi, M., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29-44. https://doi.org/10.1016/j.accinf.2017.03.003

Arafat, Y. (2025). Business intelligence as a strategic asset: Measuring its role in enhancing US national interests across defense, trade, and cyber domains. Journal of Business Insight and Innovation, 4(1), 1-20.

Asif, M., & Sandhu, M. S. (2023). Social media marketing revolution in Pakistan: A study of its adoption and impact on business performance. Journal of Business Insight and Innovation, 2(2), 67-77.

Asif, M., & Shaheen, A. (2022). Creating a High-Performance Workplace by the determination of Importance of Job Satisfaction, Employee Engagement, and Leadership. Journal of Business Insight and Innovation, 1(2), 9-15.

Basharat, R., Javaid, A., Alim, I., Khan, A. H., & Arif, N. (2025). Strategic Innovations and Transformative Impact of Blockchain Technology. The Asian Bulletin of Big Data Management, 5(2), 87-103. https://doi.org/10.62019/sc4xdv41

Bhardwaj, S., Narla, S., Absur, M. N., Dutta, S., Hyzil, J. B., & Ganesh, S. K. (2025, February). Augmented Reality-Assisted Pediatric Surgery: A Machine Learning-Based Approach for Enhancing Accuracy. In 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT) (pp. 832-836). IEEE.

Butt, S., & Yazdani, N. (2023). Implementation of Quality Management Practices and Firm's Innovation Performance: Mediation of Knowledge Creation Processes and Moderating role of Digital Transformation. Pakistan Journal of Humanities and Social Sciences, 11(4), 3881-3902.

Chowdhury, F., Sayem, M. A., Anwar, A. S., Dutta, S., Sikder, R., & Kar, M. R. (2025). Pathfinder AI: Decentralizing Career Mobility for the Underserved and Next-Gen Workforce. Cognizance Journal of Multidisciplinary Studies, 5(4), 756-781.

Dutta, S., Kar, M. R., & Sikder, R. (2025). Machine Learning Applications in Nanoparticle-Based Drug Delivery Systems for Telemedicine Advancements. Nanotechnology Perceptions, 21.

Eshra, S. A., Zohora, F. T., Akter, S., Rasul, I., & Hossain, A. (2025). The Role of Threat Intelligence in Preventing Financially Motivated Cyberattacks. Journal of Engineering and Computational Intelligence Review, 3(2), 20-37.

Fauz, F., Baloch, S. K., Al Prince, A., Raza, A., & Alim, I. (2025). Enhancing Power System Stability Through The Implementation Of Advanced Control Strategies. Spectrum of Engineering Sciences, 3(8), 307-329.

Haque, M. R., Hossain, M. I., Ankhi, R. B., Nishan, A., & Twaha, U. (2023). Liquidity traps, digital currencies and inflation targeting: A comparative analysis of policy effectiveness in advanced and emerging economies. Inverge Journal of Social Sciences, 2(3), 148-165. https://doi.org/10.63544/ijss.v2i3.115

Kashif, S. M., & Chowdhury, F. (2024). A Comprehensive Review of Asset Management Systems: Trends, Technologies, and Future Directions. Journal of Engineering and Computational Intelligence Review, 2(2), 1-9.

Khan, R., Zainab, B., Al Prince, A., Iftikhar, M., & Raza, A. (2025). Artificial Intelligence And 6g Integration: Transforming The Digital Technology Landscape. Spectrum of Engineering Sciences, 3(6), 717-737.

Lee, C. S., Cheang, P. Y. S., & Moslehpour, M. (2022). Predictive analytics in business analytics: decision tree. Advances in Decision Sciences, 26(1), 1-29.

Niazi, S. (2024). Big Data Analytics with Machine Learning: Challenges, Innovations, and Applications. Journal of Engineering and Computational Intelligence Review, 2(1), 38-48.

Qayyum, J., Siddiqui, H. A., Al Prince, A., Ahmad, S., & Raza, M. (2025). Revolutionizing market insights through AI and data analytics: The next era of competitive intelligence. The Critical Review of Social Sciences Studies, 3(1), 3285-3302.

Reis, C., Ruivo, P., Oliveira, T., & Faroleiro, P. (2020). Assessing the drivers of machine learning business value. Journal of Business Research, 117, 232-243. https://doi.org/10.1016/j.jbusres.2020.05.053

Rezvi, R. I., Rahman, K. O., Hasan, M. A., Nasrullah, F., Nusrat, N., Jishan, S. S., & Ahmed, S. (2025). How Digital Marketing Affiliates the Digital Stores: A Deep Dive into Shopify, Amazon, Walmart, and Other E-Commerce Giants. Journal of Computer Science and Technology Studies, 7(2), 95-101.

Rowshon, M., Mosaddeque, A., Ahmed, T., & Twaha, U. (2025). Exploring the Impact of Generative AI and Virtual Reality on Mental Health: Opportunities, Challenges, and Implications for Well-being. International Journal of Mental Health Research and Global Education, 3(1), 784-796. https://doi.org/10.54660/.IJMRGE.2022.3.1.784-796

Sadia, B. U. T. T. (2020). Service quality assessment and student satisfaction in business schools: Mediating role of perceived value. MOJEM: Malaysian Online Journal of Educational Management, 9(1), 58-76.

Selvarajan, G. (2021). Leveraging AI-enhanced analytics for industry-specific optimization: A strategic approach to transforming data-driven decision-making. International Journal of Enhanced Research In Science Technology & Engineering, 10(1), 78-84.

Sultan, S., Mumtaz, A., Alim, I., Javaid, A., & Arif, N. (2025). Ai-Driven Cybersecurity: Protecting Data And Privacy In An Evolving Digital World. Spectrum of Engineering Sciences, 3(7), 853-875.

Twaha, U. (2018). Analyzing General Banking Activities and Customer Service of Shahjalal Islami Bank Limited. UIU Institutional Repository. https://dspace.uiu.ac.bd/handle/52243/466

Twaha, U., Mosaddeque, A., & Rowshon, M. (2025). Accounting Implications of Using AI to Enhance Incentives for Wireless Energy Transmission in Smart Cities. International Journal of Management Research and Global Education, 6(2), 1208-1218. https://doi.org/10.54660/.IJMRGE.2025.6.2.1208-1218

Ullah, A., & Khan, S. D. (2024). Impact of sound decision-making on small and medium businesses in Pakistan. International Journal of Asian Business and Management, 3(2), 177-192.

Verma, R., & Pandiya, D. K. (2024). The role of AI and machine learning in US financial market predictions: Progress, obstacles, and consequences. International Journal of Global Innovations and Solutions (IJGIS), 4(1).

Zong, Z., & Guan, Y. (2025). AI-driven intelligent data analytics and predictive analysis in Industry 4.0: Transforming knowledge, innovation, and efficiency. Journal of the Knowledge Economy, 16(1), 864-903. https://doi.org/10.1007/s13132-023-01289-7