Economic and financial crimes have recently evolved in a dramatic way. Through illegal practices such as tax evasion, corruption, money laundering, smuggling, counterfeiting, but also through drugs, weapons of mass destruction, migrants, cultural property and protected species, economic and financial crimes show the destructive financial power of criminal groups and white-collar criminals.
Given that financial institutions (FIs) are often seen as the international conduit/channel of financial crimes, most of the states have imposed strict regulations and procedures on financial institution to detect and report suspicious financial crimes including money laundering. The implementation of these procedures is considered today as one of the most expensive regulatory guidelines that FIs must comply with (Barry and Ian, 2006, p. 48). These financial institutions mobilize complex systems and triage teams to filter and analyze large amounts of data to detect suspicious transactions. These systems and processes that are currently in place have been found ineffective given the trend of fines imposed on international banks (Deloitte, 2015).
FIs need to implement more effective systems to discharge their responsibilities in terms of fight against money laundering practices and reporting obligations vis-à-vis the state and other international bodies. One of the promising disciplines in this area are Advanced Analytics (AA) and Artificial Intelligence (AI) (Alvarez et al. 2017, p. 2). AA and AI can be described as a constellation of technologies that enable machines to act with superior intelligence and imitate human capabilities to detect, understand, and act. These technologies can enable FIs to automate their processes seamlessly to improve frontline productivity and open new revenue streams to stay competitive. Similarly, it can help improve FIs’ capacity for risk management and anti-money laundering (AML) techniques, credit allocation and fraud detection, as well as increase their share of sales and digital transactions (Alvarez et al. 2017, p. 3).
This research finds its genesis through the following two points:
• Actual implementations of Money Laundering rules and regulation. I have had the opportunity, through my professional career, to work in the risk and governance areas in different financial institutions. Depending on the geographical location, the regulations are very stringent, and banks must implement compliance programs that are extremely expensive.
• Interests in Advanced Analytics and Artificial Intelligence. Competition is fierce in the banking sector. Banks offer the same products with same pricing. Differentiation is mainly through customer service and innovation. In order to be profitable, banks must undergo efficiency programs and given that AML compliance programs are costly, banks need to implement innovative AML techniques, such as Advanced Analytics, which have been proven to be cost effective.

The research question is therefore formulated as follows:
Anti-money Laundering Techniques: Why Advanced Analytics and Artificial Intelligence should be the Cornerstone of an Effective Detection Mechanism in the Financial Sector?
Few authors have explored the cost of implementing AML techniques on financial institutions, but many are unanimous around the complexities and weaknesses of actual measurement techniques.
The idea that Advanced Analytics and Artificial Intelligence can be useful and relevant against money laundering practices has been reinforced in the work of Fathian & al. (2017). Deficiencies in the effectiveness and efficiency of current techniques can be overcome as stakeholders will have more insight into risk perception. Earlier attempt by The Financial Crimes Enforcement Network AI System (FAIS) in 1995 also yielded the same conclusion, which is the superiority of AA and AI in effectively detecting suspicious transactions. However, not many FIs have implemented these techniques.
The objective of this thesis is to highlight the main challenges facing banks in anti-money laundering (AML) operations today and how compliance can be addressed with the adoption of AA and AI. It will examine how these techniques are superior and cost effective over rule-based analysis which is used predominantly by majority of FIs. Subsequently, the dissertation will attempt to highlight some of the technological and organizational challenges that the FIs will face in the implementation of AA and AI. Finally, it will propose some recommendations, particularly regarding data management and governance.

The methodology proposed in this thesis is divided into two phases. The first phase consists of a literature review to identify and define the concepts related to AA and AI and the fight against money laundering. We will be analysing documents, some known cases and publicly available reports, in addition to academic resources. To this end, the following search engines were used:

1. University library
2. Google Scholar – the scientific search engine of Google
Regarding Advanced Analytics and money laundering, the following terms have been crossed: “Advanced Analytics”, “Big Data”, “Machine Learning”, “Rule-based Money Laundering”, “False Positives”, “Anti-Money Laundering”, “Compliance Function”, “Financial Institutions/Banks”. Additional articles have been identified through consultation of the bibliographical sections of key articles used in this thesis as well recommendations from my supervisor.

The second phase is that of analysis. Implementation of AI and AA will bring structural changes in the organization and this analysis will clarify, amongst other things, the need for a new architecture (Gao et al. 2006, p. 853) and the new mandate of the compliance function and various interlinked support functions (James Wilson et al. 2017, p. 15).

Structure of the thesis:
This thesis is structured as follows:
I. Introduction
a. Advanced Analytics and Artificial Intelligence: a response to deficiencies in the fight against money laundering
b. Absence of international enforcement
c. Consequence of disregarding Advanced Analytics and Artificial Intelligence
d. Methodology and thesis structure
II. Analysis of current AML techniques and their limits
III. The contribution of Advanced Analytics and Artificial Intelligence in the fight against money laundering
IV. The implementation of Advanced Analytics and Artificial Intelligence
a. Operational use cases
b. Pre-requites to an effective implementation
c. Legal framework
V. Conclusion


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