Assessing the Impact of Machine Learning Model on Identifying Terrorism Financing Patterns within the U.S. Financial Industry

Matthew Oman-Amoako; Victor Boateng; Elizabeth Amoako; Mildred Bonsu

doi:10.36948/ijfmr.2025.v07i06.62486

Assessing the Impact of Machine Learning Model on Identifying Terrorism Financing Patterns within the U.S. Financial Industry

Author(s)	Matthew Oman-Amoako, Victor Boateng, Elizabeth Amoako, Mildred Bonsu
Country	United States
Abstract	The increasing complexity and sophistication of terrorism financing have outpaced traditional monitoring systems within the U.S. financial industry, creating a need for more advanced, data-driven detection tools. Although machine learning (ML) is widely recognized for its potential to improve anomaly detection and behavioral profiling, there is limited empirical research on its actual application and effectiveness in fighting terrorism financing. This paper aims to carefully evaluate how machine learning models are currently used in U.S. financial institutions to identify terrorism financing patterns and whether these efforts contribute meaningfully to achieving Goal 16 of the United Nations Sustainable Development Goals (Peace, Justice, and Strong Institutions). A qualitative research approach was used, including a thorough review of empirical studies, existing literature, industry cases, and analysis of documents from regulatory agencies and industry white papers. Our findings show that though ML shows promise in recognizing patterns and enabling early detection, its implementation is heavily limited by regulatory compliance requirements, privacy laws, technological fragmentation and institutional risk aversion. Additionally, most institutions lack access to timely and adequate training data, especially for rare or evolving threat scenarios. The study concludes that the current environment in the U.S. financial sector leads to a performance-constrained calibration of machine learning models, where institutional, legal and infrastructural barriers prevent full optimization. Therefore, achieving the full potential of ML in this area will require more collaborative frameworks among regulators, institutions, and technology providers, along with standardized data-sharing protocols and national-level integration strategies.
Field	Computer Applications
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-12-04
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.62486

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Assessing the Impact of Machine Learning Model on Identifying Terrorism Financing Patterns within the U.S. Financial Industry

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