Artificial Intelligence in Anti-Money Laundering: Opportunities, Threats, and the Operational Challenges Facing Financial Institutions

Castillo Gonzalez, Erica (2025) Artificial Intelligence in Anti-Money Laundering: Opportunities, Threats, and the Operational Challenges Facing Financial Institutions. Masters thesis, Dublin, National College of Ireland.

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Abstract

This study explores how financial institutions are currently leveraging artificial intelligence (AI) in the fight against money laundering criminal organisations. It examines the extent to which AI serves as a powerful tool for identifying patterns, behaviors, and warning signals indicative or illicit activities within an increasingly interconnected global banking system.

The research outlines the three stages of money laundering, highlights activities identified as illicit by international organisations and discusses the consequences of criminal groups continuing to bypass regulatory filters and infiltrate the global financial market. It also explores the day-to-day challenges faced by banking institutions in managing such threats.

Furthermore, this study seeks to provide evidence supporting the argument that AI can be a valuable tool in accelerating the detection of suspicious transactions, reinforcing the security filters of the global financial system, and reducing the proportion of criminal funds circulating within it. Most importantly, it explores how improving the fight against money laundering can lead to a significant reduction in the financial resources available to criminal organisations engaged in activities such as human trafficking, illegal drug trade, terrorism, and other serious offences.

This research provides a relevant contribution by examining practical insights from industry professionals in the context of global regulatory frameworks and technological advancements in AML. By aligning first-hand perspectives with existing international developments, the study aims to highlight connections between operational realities and broader policy and innovation trends, offering a grounded understanding of how artificial intelligence can enhance and potentially challenge AML frameworks.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Walsh, Jeffrey UNSPECIFIED
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare > Criminology > Crimes and Offences H Social Sciences > HG Finance Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence
Divisions:	School of Business (- 2025) > Master of Science in Entrepreneurship
Depositing User:	Ciara O'Brien
Date Deposited:	11 Feb 2026 12:50
Last Modified:	11 Feb 2026 12:50
URI:	https://norma.ncirl.ie/id/eprint/9119

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