Conceptual modeling and development of an intelligent agent-assisted decision support system for anti-money laundering Shijia Gao * , Dongming Xu UQ Business School, University of Queensland, Brisbane, QLD 4072, Australia Abstract Criminal elements in today’s technology-driven society are using every means available at their disposal to launder the proceeds from their illegal activities. In response, international anti-money laundering (AML) efforts are being made. The events of September 11, 2001, highlighted the need for more sophisticated AML and anti-terrorist financing programs across the industry and nation. In the wake of this, regulators are focusing on the role that technology can play in compliance with laws and ultimately in law enforcement. Banks will have to employ or enhance AML tools and technology to satisfy rising regulatory expectations. While many AML solutions have been in place for some time within the banks, they are faced with the challenge of adapting to the ever-changing risks and methods related to money laundering. In order to provide support for AML decisions, we have formulated an AML conceptual model by following Simon [Simon, H. A. (1977). The new science of management decision. Englewood Cliffs, NJ: Prentice-Hall] decision-making process model. Based on this model, a novel and open multi-agent AML system prototype has been designed and developed. Intelligent agents with their properties of autonomy, reactivity, and proactivity are well suited for dynamic, ill-structured, and complex ML prevention controls. The advanced architecture is able to provide more adaptive, intelligent, and flexible solution for AML. This paper is the first attempt at intelligent agent financial application in the AML domain, with a decision-making/problem-solving process model, an innovative agent-based architecture, and a prototype system. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Anti-money laundering; Decision support systems; Conceptual model; Intelligent agents; Business intelligence application 1. Introduction Since the mid-1980s, money laundering (ML) has been recognized as a significant global problem with serious eco- nomic and social ramifications (Camdessus, 1998). The sheer magnitude of money laundering is such that it now ranks as one of the gravest criminal threats to the global community, capable of corroding international financial systems and corrupting entire democracies (IMoLIN, 1998). Today, ML has become a key funding mechanism for international religious extremism and drug trafficking. Curtailing these illegal activities has become an important focus of governments as part of their ongoing wars on ter- rorism and drug abuse. The international community has made major strides in the fight against ML, most notably through the work of the Financial Action Task Force (FATF). Its recommendations have strengthened the regu- latory framework aimed at stemming the flow of ‘‘dirty money.” Globalization, exponential growth in transactions and accounts, and criminal creativity all combine to chal- lenge current ML efforts. Following the terrorist acts of Sep- tember 11, 2001, there has been an increased focus in the United States, and across the globe, on the prevention of ML and terrorist financing. Governments and law enforce- ment agencies have called on the financial services industry to be vigilant in helping to identify potential sources of ter- rorist financing. In the United States, these efforts have included passage of the USA PATRIOT ACT—legislation that contains major new ML provisions. Diversified regula- tions, guidelines, and laws have been issued by govern- ments, involved organizations and institutions around the 0957-4174/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2007.11.059 * Corresponding author. Tel.: +61 7 3346 9323; fax: +61 7 3365 6988. E-mail address: c.gao@business.uq.edu.au (S. Gao). www.elsevier.com/locate/eswa Available online at www.sciencedirect.com Expert Systems with Applications 36 (2009) 1493–1504 Expert Systems with Applications