Following almost two years spent on the Financial Action Task Force’s (FATF) so-called ‘grey list’, which identifies countries with notable deficiencies with regard to their anti-money laundering (AML) regulations, the United Arab Emirates made significant efforts to enhance its financial integrity and regained its status as a trusted partner in the world of international finance in February 2024. This trust, however, must be actively nurtured going forward too, and while the UAE engaged in impressive legal reforms while it was on the ‘grey list’, these must be consolidation both within domestic institutions and across the country’s dealings in the international financial community.
Artificial intelligence (AI) provides various practical tools that can serve as critical enablers that empower financial institutions to swiftly detect and even pre-empt financial crime.
The challenges of sophisticated financial crime
Based on the FATF’s assessment, the UAE government did not espouse a sufficient understanding of the risks associated with money laundering in key sectors of the economy while its financial intelligence practices were not thorough enough. The economic impact of grey listing is tangible, with both foreign direct investment and trade volumes declining as a direct result.[i] According to the International Monetary Fund (IMF), grey-listing can reduce capital inflows by up to 7.6% of GDP.[ii] The UAE responded with over 50 reforms to assuage concerns and restore the investor trust, which included the establishment of a dedicated Executive Office for anti-money laundering and countering the financing of terrorism, a crackdown on unlicensed hawala providers (running an informal money transfer system), and increased supervisory inspections of transactions and deals.
But the reality is that financial crime has become increasingly sophisticated, leveraging trade-based money laundering (TBML), complex transaction layering techniques, as well as the opacity of virtual assets to avoid detection by authorities. Traditional rules-based systems are not agile enough to detect these methods in most cases. They are reactive by design and prone to a high ration of identifying false-positive cases, which, in turn, burden inspector teams further and delay regulatory responses too.
The delisting of the UAE by the FATF does not signal the end of risks in its financial networks. On the contrary, countries that successfully repair their status attract renewed scrutiny to ensure that their reforms are durable and effective. For the UAE, where the real estate sector, gold trading, and virtual assets play a significant role in the economy, the road ahead demands not just policy but precision, and tools that can adapt as fast as the threats evolve. These sectors must also be anchored not only in vigilance, but in the government’s capacity to scale compliance efforts, institutionalize transparency, and build economic confidence in the long term.
AI revolutionizes finance and financial crime detection
AI changes the equation, introducing tools that can analyze billions of data points, learn from emerging patterns along the way, and flag abnormal behaviors that do not conform to previously prevalent trade and investment flows. In a market like the UAE, where cross-border trade and investment flows through tangible as well as digital assets, including trade in gold and other precious metals and minerals, this kind of AI-enhanced approach is critical.
The UAE is the largest gold trading hub in the Middle East and among the top five globally, importing approximately $24 billion worth of gold in 2024[iii], much of which was sourced from high-risk geographies without ESG principles overseeing the transactions. The gold trade has long been associated with trade-based money laundering because it hides transactions behind minimal documentation, attribution to individuals or companies, and few standardized compliance checks. AI can help detect inconsistencies in trade patterns, identify circular trading routes, and flag suspicious shipments that deviate from standard trading patterns, or trade flows backed up by legitimate business endeavors.
Virtual assets provide another avenue for financial crime to proliferate. The UAE has taken important steps through the Virtual Assets Regulatory Authority (VARA) to regulate this sphere of finance, but a next-generation approach is required to crack down on money laundering here too. AI-powered solutions can monitor suspicious behavior in digital wallets, identify clusters of unusual transactions, and interactions with high-risk, offshore entities that can conceal the purpose of the flow of money and its intended recipients.
AI is also foundational for enabling risk-based supervision, a core principle of the UAE’s national AML strategy. Rather than applying uniform scrutiny, risk-based models allocate resources where they are needed the most. This requires deep, real-time data analytics, which only high-powered AI tools can provide at the scale of the UAE economy.
From the UAE to the broader Gulf and MENA region
In the UAE, where regulatory compliance and international trust are again key priorities, AI solutions have the potential to significantly reduce false positive cases and uncover complex money laundering schemes that often evade traditional methods of detection. Leading institutions such as Mashreq Bank are already at the forefront of this field, having deployed Cognitive AI technologies like ThetaRay’s to strengthen their AML frameworks and set a precedent for the entire region’s financial sector.
The UAE’s strides in the field of developing AML-oriented AI tools can also serve as the bedrock for a broader, regional AI governance system, that pools best practices and tools for the mutual securing of financial flows. Saudi Arabia, Bahrain and Israel, but event Western countries such as the United Kingdom and the United States are among the potential partners that would benefit from seeing a regional architecture of financial regulation and implementation emerge.[iv]
The UAE must turn the current momentum into leadership. It has already demonstrated it can respond to FATF pressure with speed and at scale. The next step is to future-proof the gains made over the past few years and embrace the technological backbone that can secure compliance for decades to come.
[i] Anderson, J., Cohen, J.M. and Luna W. (2023). ‘The Economic Impact of FATF Grey-Listing’, White&Case, 30 October 2023, retrieved from: https://www.whitecase.com/insight-alert/economic-impact-fatf-grey-listing.
[ii] Kida, M. and Paetzold, S. (2021). ‘The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning’, IMF eLibrary, 27 May 2021, retrieved from: https://www.elibrary.imf.org/view/journals/001/2021/153/article-A001-en.xml.
[iii] Voronoi (2025). ‘Top Gold Net Importers and Exporters in 2024’, 10 April 2025, retrieved from: https://www.voronoiapp.com/markets/-Top-Gold-Net-Importers-and-Exporters-in-2024-4676.
[iv] Cambridge Middle East and North Africa Forum (MENAF) 2025). ‘UK AI Diplomacy: Boosting British Influence in MENA and Strengthening the Abraham Accords’, April 2025, retrieved from: https://cmenaf.org/research-uk-ai-diplomacy-boosting-british-influence-in-mena-and-strengthening-the-abraham-accords/.
