Journal of Primeasia
Integrative Disciplinary Research | Online ISSN 3064-9870 | Print ISSN 3069-4353
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Journal of Primeasia

Volume 6 Number 1 2025

Article Contents

RESEARCH ARTICLE   (Open Access)
Contents Vol 6 (1)

Integrating Artificial Intelligence and Machine Learning into U.S. Financial Risk Management Systems

Mitu Akter1*, Md. Rezaul Haque2, Md Iqbal Hossain3

+ Author Affiliations

Journal of Primeasia 6 (1) 1-8 https://doi.org/10.25163/primeasia.6110430

Submitted: 03 September 2025 Revised: 04 November 2025  Published: 13 November 2025 

Abstract

Background: The financial industry operates under multiple complex risks which stem from market fluctuations and operational breakdowns and credit payment failures and digital security breaches. Traditional risk management systems base their predictions on linear models which use past data but this approach produces inaccurate forecasts. Artificial Intelligence (AI) systems together with Machine Learning (ML) models operate through data-based adaptive techniques which boost risk detection capabilities and predictive accuracy and operational productivity.

Methods: A structured questionnaire was administered to 310 financial professionals across commercial banks, investment firms, and regulatory agencies.AI models, including Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks, were implemented and compared with traditional regression models. The evaluation of performance used prediction accuracy and R² and correlation (r) and statistical significance (p) to compare results with questionnaire responses.

Results: The survey showed that 68% of participants achieved better prediction results and 54% experienced improved operational performance through their AI implementation. The LSTM model showed the best results when compared to other AI and traditional models by reaching a correlation value of 0.82 with observed outcomes at p=0.004. AI system integration into risk management processes led to a 27-41% decrease in decision-making time and a 32-38% increase in fraud detection effectiveness which produced both statistical and operational benefits.

Conclusion: The integration of AI and ML technologies into financial risk management systems produces better prediction accuracy and improved operational performance and faster decision-making.

Keywords: Artificial Intelligence, Machine Learning, Financial Risk Management, Predictive Analytics, Fraud Detection

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