Hershey, Pennsylvania
New York, New YorkBeijing, China
Artificial Intelligence for Money Laundering Detection

Artificial Intelligence for Money Laundering Detection

Fouad M. Ziade (Lebanese University, Lebanon), Malak Mohamad Daher (Jinan University, Lebanon), and Abdallah M. Ziade (Lebanese University, Lebanon)
Copyright: © 2024 |Pages: 12
DOI: 10.4018/979-8-3693-1046-5.ch003
OnDemand:
(Individual Chapters)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

This chapter provides an overview of artificial intelligence (AI) methods for anti-money laundering (AML) and discusses challenges. The increasing complexity of financial crime has exposed the limitations of traditional rule-based AML approaches. AI technologies like machine learning, natural language processing, and computer vision show promise for improving AML effectiveness and efficiency. However, AI also faces hurdles around data quality, model interpretability, ethics, and proper human-AI collaboration. The chapter reviews the state-of-the-art AI techniques being applied across AML domains including customer due diligence, transaction monitoring, risk scoring, and investigations. Key recommendations for implementing AI in practice involve extensive testing, explainable models, strong governance, and human-centered design focused on trust and transparency. While AI has limitations, thoughtful deployment focused on fairness, accountability, and empowering human expertise can allow financial institutions and regulators to realize its benefits for combating money laundering.
Chapter Preview
Top

1. Introduction

1.1. Money Laundering and Its Impact on Society and Economy

Money laundering is the process of disguising the origins of money obtained through illegal activities by funneling it through legitimate businesses (Levi, 2021). It allows criminals to hide and accumulate wealth, avoid prosecution, and continue illicit operations (Masciandaro & Takats, 2007). Money laundering has become a global issue, with estimates that 2-5% of global GDP is laundered money annually (Schneider, 2010). This illicit financial activity has detrimental effects on economic growth, governance, and societal welfare.

Money laundering distorts asset and commodity prices and allows criminal enterprises to continue profitable operations (Reuter, 2013). It provides resources for terrorism, drug, and human trafficking, and public corruption (Ferwerda, 2013). The influx of dirty money creates unfair competition for legitimate businesses, misallocates resources, and decreases productivity (Unger et al., 2006). Money laundering also erodes public trust in financial institutions that are exploited and increases the costs of compliance and monitoring (Barone & Masciandaro, 2011).

Governments worldwide have enacted anti-money laundering regulations to detect, deter, and prevent this financial crime (Sharman, 2010). However, the increasing complexity and integration of the global financial system provide opportunities for laundering and make enforcement difficult (Ngai, 2011). More research is needed to understand evolving techniques and effects of money laundering to inform evidence-based policies. With vigilance and coordinated efforts across borders, the harm of this illicit activity can be mitigated.

The Main Ideas Discussed in This Chapter

  • ▪

    To provide an overview of the current state-of-the-art artificial intelligence (AI) methods for anti-money laundering (AML) and their applications in different domains and contexts

  • ▪

    To discuss the main challenges and opportunities of using AI for AML, such as data quality and availability, explain ability and transparency, ethical and legal issues, and human-AI collaboration.

  • ▪

    To offer recommendations and best practices for implementing AI for AML in practice and to identify the key directions for future research and development in this field.

Complete Chapter List

Search this Book:
Reset