Artificial Intelligence: a tool for financial institutions in the fight against money laundering and financial crime

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Artificial Intelligence: a tool for financial institutions in the fight against money laundering and financial crime

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Themes

AI artificial intelligence, AML antimoney laundering, tax fraud, tax evasion, tax optimization, terrorist financing, communication policy, good governance, OECD initiative, financial crime, bank, digital transformation, data, general public, control

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Abstract

This document contains a presentation that was used during a thesis defense. It tackles how artificial intelligence can be a tool for financial institutions in the fight against money laundering and financial crime.

Introduction
Money laundering and financial crime worldwide: a literature review
1. Tax havens, money laundering worldwide and the role played by banks
2. The various forms of financial crime
3. The development of crypto-assets, a source of financial crime and money laundering
Evolution of digitalization and artificial intelligence in finance
1. The digital transformation of banks
2. Use of AI by tax authorities to detect fraud
Quantitative study on the rôle played by AI for financial institutions in fraud detection
1. Research question
2. Methodology, sample and questionnaire
3. Analysis of results
Discussion
1. Challenges for AI in the fight against money laundering and tax fraud
2. Mastering massive volumes of data in banking, precious A.I resources: the fundamental role played by management controllers

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[...] Quantitative approach to evaluate and weight the behaviors, motivations as well as opinions of banking professionals, in terms of anti-money laundering and fraud detection, through the use of AI Methodology, sample and questionnaire 10 questions, divided into three themes: financial fraud in financial institutions (questions 1 to ; knowledge of AI technology (questions 5 to 15 Sample: 11 professionals, working in financial institutions (bank, asset management company or investment company) and holding a variety of positions (individual client advisor, professional advisor, wealth management advisor, financial analyst). Average age: close to 29 years old 4.3 Analysis of results Theme financial fraud in financial institutions - There is a high incidence of fraud in financial institutions, particularly banks. [...]

[...] Fines awarded to laundering dispute Banking institution fined Amount of fine imposed banks following money Year (in HSBC ING Bank Crédit Suisse Lloyds Banking Group PLC Standard Bank PLC The various forms of financial crime Development of financial crime in a deregulated financial system Financial innovations (derivatives, securitization, high-frequency trading, crypto-assets complex financial products promote total opacity and tax evasion Individual fraud (Ponzi, Madoff, Kerviel ) and institutional fraud Types of problem for actors (magistrates, senior civil servants, public finance officers, auditors ) in charge of combating money laundering and financial crime: insufficient human resources; 7 a lack of genuine political will. [...]

[...] Technologies and applications encompassed by digitization: AI, data analytics, blockchain, virtual and augmented reality, the cloud and robotics. Multiple distribution channels for the bank: the omnichannel Challenges posed by digitization for banks: - massive investments to ensure universal accessibility; - specialized IT skills to maintain and develop software on an ongoing basis; training costs and hiring of additional staff. - risk of loss of data and information requires maximum security protection9 of 3.2 AI applications in finance: a tool in the fight against money laundering and financial crime Banks pioneering AI: Bank of America, Deutsche Bank; Crédit Mutuel (since 2017) Additional revenues, generated by AI, a source of Net Banking Product and productivity gains (ACPR Improved bank profitability, reduced costs and improved revenue streams Better management and monitoring of customer portfolios, more efficient and proactive customer advisors, daily administrative and regulatory tasks carried out by AI Enhanced customer segmentation, with mass variables processed quickly and 10 efficiently, resulting in a consistent prospecting file Digital surveillance, via AI, to combat money laundering and financial crime The current bank fraud detection monitoring system and its weaknesses: Semi-automatic systems for detecting suspicious transactions Transaction watch list filtering (Alkhalili, Qutqut and Almasalha ) Transaction monitoring systems at financial institutions Source: Desrousseaux (2022) 11 • Two major problems posed by this monitoring process based on rules: rules and parameters are set, which requires skilled personnel in this field. [...]

[...] Accessibility of AI to the general public, and how it works (Banque de France, 2020). AI is still in its infancy as a means of detecting money laundering and financial crime Further experimentation and testing are needed before comprehensible rules can be extracted automatically Mastering massive volumes of data in banking, precious A.I resources: the fundamental role played by management controllers The emergence of Big Data and artificial intelligence is therefore driving controllers to invest heavily in the technical functions of data collection, control and reliability. [...]

[...] Most of them feel that their institution is not sufficiently secure in the face of the various threats posed by financial crime. - Training in compliance and the fight against money laundering, and knowledge 16 of their institution's commitments in this area. Theme knowledge of AI technology AI is part of our sample's environment, particularly when it comes to KYC. They just don't have the technical skills to use it regularly in their activities. [...]

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