The new normal of digital banking post-Covid-19 is driving fintech growth in the UAE, where online payment platforms are improving accessibility to help resolve a pressing need for financial inclusion.  Cross-border payments are a necessity in the UAE where migrant workers make up 90% of the workforce. Outflows from the UAE reached $43.2 billion in 2020, second only to the US, according to the 2022 World Migration Report. So, it’s no coincidence that the UAE has become one of the world’s top fintech centers with neo-banks, Open Banking, mobile wallets, and app-based international money transfers vying for the business of residents. Cross-border payments are a necessity in the UAE where migrant workers make up 90% of the workforce. Outflows from the UAE reached $43.2 billion in 2020, second only to the US, according to the 2022 World Migration Report. The UAE economy was traditionally based on international trade of oil and gas, but now the economic strategy has shifted to making the country into a global financial hub with robust banking services and innovative fintech technologies. Government-supported initiatives include the DIFC innovation hub, as well as the ADGM international financial center. Global headwinds  Amid this positive momentum, a year ago the UAE was placed on the “grey list” of the Financial Action Task Force (FATF), the global anti-money laundering watchdog, for countries in need of “increased monitoring” of their AML/CFT regime.  The UAE succeeded in implementing many improvements and changes in one year, but it is still on the list. Some of these changes, naturally, are putting a lot of pressure on fintechs and bank executives.  And that’s on top of the challenges now facing the entire global economy. The UAE grey list status has created new challenges for financial institutions from fintechs to banks. Renewed challenges for fintechs The increased global scrutiny of regulators is creating a setback for fintech relationships that remain dependent on correspondent banking relationships and infrastructure to make cross-border payments.  The value proposition of fintechs is their ability to deliver on speed, cost, and accessibility. For fintechs to fulfill this mission for their end-customers, they need to have favorable agreements in place with banks, holding their accounts, to settle and reconcile between balances to credit their customers, in order to lower costs. Banks realize the value of fintech relationships, and are open to doing business for certain types of payments that fintechs can do more cost-effectively, while they maintain revenue from the infrastructure. Still, fintechs are traditionally considered less strict in customer due diligence (CDD) compared with banks and are perceived as higher risk. Within normal business relationships between fintechs and banks, pseudo-customers identities will not be apparent in bulk payments handled by banks, making it essential that fintechs effectively monitor transactions for suspected financial crimes and screen customers against watchlists. Under the old normality, even if they had great technology, fintechs suffered from mountains of challenges, including barriers and expenses from the government, regulators, and banks. As a result, they only reached a fraction

Financial institutions are seeking more advanced technology that can protect their networks against money laundering and cybercrime and enable them to increase profits. ThetaRay is rising to the challenge of helping banks and fintechs with unique and powerful AI (Artificial Intelligence) solutions that can imitate human intuition. Today’s financial crime is sophisticated cybercrime, and it is much more severe and widespread than in the past. Criminals no longer need to take risks, they can simply stay at home and use the advanced technology available to everybody to launder money, finance terrorism, or engage in human or drug trafficking. One of the reasons for the increased severity of the risks faced by banks and payment services providers is the digitization of authentication processes triggered by the Covid-19 crisis. Today, bank accounts are opened digitally, unlike in the past when customers had to appear in person at the bank. Consequently, the ability of banks to authenticate the identity of customers is significantly reduced. Moreover, when a bank transfers funds between branches or countries, it is exposed to significant risks as it has no way of verifying identities along the entire transfer chain. Providing customers full trust Alongside the developments in financial crime and its sophistication, regulations are becoming stricter.  Legacy rules-based tools are no longer capable of contending with the complexity of a large number of players in the financial system with the boom in fintech and the new possibilities available for transferring funds. This applies to hundreds of countries and dozens of types of currency, not to mention new cryptocurrencies entering the global financial stage. Fintechs offer less expensive services, which among others, are used by migrant workers to send money home. These companies require solutions to enable them to obtain an entry ticket into the world of financial regulation, along with the high degree of credibility required for money transfers.   AI has changed the rules of the game. It is precisely here where ThetaRay enters the picture. Using a sophisticated form of AI, “artificial intelligence intuition,” it is able to understand what is normal within big data and what is abnormal. The ThetaRay AI engine analyzes the data and consequently understands the behavior of the entities. It labels the unusual transfers that are outside the norm, and all this is accomplished automatically, without the need to teach the system what an unusual/normal transfer looks like. In each proof-of-capability we perform for a bank or a fintech, we find a high number of deviations from the norm, including typologies unknown to the bank or payment fintech.   ThetaRay monitors more than 11 billion transactions a year with valued at over  $15 trillion, belonging to more than a billion accounts worldwide. The solution delivers a 95% level of precision in identifying investigation-worthy cases compared with the legacy solutions offering a rate of only 5%-30%. The number of false positive alerts generated by ThetaRay is up to 99% lower than the existing, legacy solutions, and accordingly, it can help reduce the amount

The uphill battle against money laundering and financial crime in 2022 was marked by progress in some areas but offset by a mountain of new challenges.  In fact, progress in AML programs appears to be “stuck,” according to the Basel AML Index which ranks money laundering and terrorist financing risks around the world.  The 2022 report concluded that any real progress is being impeded by increased risks. No doubt these trends will continue to impact anti-money laundering efforts in 2023, as most countries continue to be too many steps behind criminals seeking to launder illicit funds.   The following are some of the negative headwinds that are weighing on compliance programs. Sanctions screening pressure Sanctions lists saw a 95% jump in updates in the first half of 2022 due to the Russia-Ukraine war.  This has caused a massive impact on compliance programs now faced with a higher stack of alerts, pressure on list updating processes, and burdensome alert remediation.  Real-time sanctions screening is a regulatory requirement and a major point of friction in the world of instant payments. Sanctions screening quickly turns into a bottleneck for financial institutions as surgical precision is required while matching a variety of imperfect and unstructured payment data against sanctions lists. Crypto is still a heyday for financial crime  Financial criminals continued to flow to cryptocurrency and other virtual assets in 2022 to innovative methods of money laundering. The Basel report called the AML risks from virtual assets “worrisome” with compliance oversight dropping dramatically. In one case that was uncovered last month, two Estonian citizens were arrested in a $575 million cryptocurrency fraud and money laundering scheme. Data breaches   Digital authentication hazards continue to challenge the financial system, as face-to-face identification is no longer a requirement to open a digital or mobile bank account. Criminal sophistication continues to cause data breaches and identity theft, where information can end up in money-laundering schemes.  The ease of opening bank accounts and the surge in the number of financial ecosystem players complicate the ability of the financial ecosystem to detect the true identities of the people and the activity behind them. Major data-compromising incidents and new patterns were recorded globally in 2022. In South Africa, credit bureau TransUnion SA suffered a cyberattack that saw data stolen from around three million customers by a criminal third party. In South Korea, researchers identified a new “fake calls” banking trojan. The bot has the ability to “talk” to victims and pretend to be an employee of the bank in order to gather personal information.  In the United States, 2.5 million social security numbers were stolen in a student loan data breach at Nelnet Servicing. In positive developments, measures are being taken to improve AML/CFT capabilities. Banks and fintech are continuing to adopt new AML solutions powered by advanced AI technology to get out of the rut. Indeed, according to the Basel report, the highest level of progress was achieved with the involvement of the private sector. Here are areas where AML is

Compliance professionals have surely had a very stressful year in 2022 with the extra burden of transaction screening work, amid a 95% jump in sanctions list updates due to the Russia-Ukraine war. This regulatory pressure is not expected to fade away in 2023 with new sanctions packages being approved and sanctions evasion set to become a crime in the EU as the bloc continues to seek ways to punish Russia over its war in Ukraine. Real-time sanctions screening is a regulatory requirement and a major point of friction in the world of instant payments. Fintechs, whose mission is to deliver a better customer experience, speed, and reduce friction, are discovering that sanctions screening quickly turns into a bottleneck for their fast-growing business. The increasing complexity of screening The complexity of sanctions screening is not only caused by the volume or frequency of the updates of different uncoordinated regulatory bodies. It is also the story of the surgical precision required while matching a variety of imperfect and unstructured payment data against sanctions lists. Regulators expect more than perfect/exact matches, increasing expectations each year for sanctions screening frameworks. As a result, screening software is relied on to analyze misspellings, different alphabets, transliterations, and, more recently, geographical challenges to effectively match real-time transactions. Recent fines leveraged against fintechs highlight an urgent need to ramp up their sanctions screening game. Choose your perfect match Choosing a sanctions screening solution in this stormy journey is key. Surprisingly, there are not so many screening solutions providers with good technology tailored to fintech needs that provide good value for money as well as company culture fit. Legacy systems trigger large volumes of false positive alerts, often more than 95%. While balancing risk and cost, the goal is to cover all possible risks. However, fintech will incur high costs with the need to review false hits that could be like 297/300, all while slowing down business. In addition, incumbent legacy providers often do not have customer-centricity in their DNA while having prohibitive prices adapted to the compliance budgets in the banking sector. Those drivers make fintech consider a more sophisticated, cloud-native AI-based technology that is better equipped to overcome challenges for both sanctions screening and transaction monitoring. Rule-based solutions are no longer a good option for sanctions risk protection or for fintech pockets. Here are the key factors to take into consideration while choosing a screening solution: 1. Speed and infinite scalability The hectic rhythm of real-time transaction screening requires a smart architectural fit and unrivaled performance. Your tool should adapt to your scaling business in the same way it adapts to the changing sanctions risks and regulatory requirements. An AI-based solution with machine learning algorithms in the screening engine delivers extreme precision and speed in the process. It also filters out the noise that triggers false alerts. In this way, AI can replace the time-consuming work of investigators to spot true alerts and focus on real risks. 2. Magic three “E”: efficient, effective, explainable AI enables FIs

The Middle East, at a crossroads between Europe, Africa, and Asia, is playing an increasingly global role on the financial scene. Today, the United Arab Emirates (UAE) serves as a financial hub in the region, with commerce, banks, and fintech operating out of Abu Dhabi and Dubai. UAE fintech is also leading in the region, with a market value of $ 2.5 billion in 2022, according to figures from the UAE Ministry of Economy. The UAE is also a major player in the remittance market with about 90 percent of the country’s total population foreign workers or expats. Amid this boom, the UAE financial community was hit with a blow in February 2022, when the country was placed on the “grey list” of the Financial Action Task Force (FATF), the global anti-money laundering watchdog, for countries in need of “increased monitoring” of their AML/CFT regime. The deterioration to the grey list highlights the vulnerability of the UAE as a corridor for illicit transactions and sanctions evasions, notably with several other neighbors on the grey list (Jordan, Turkey, Syria, Yemen) and even blacklisted (like Iran). These issues are undoubtedly a concern for other neighbors as well such as Saudi Arabia and Bahrain. On the bright side, countries on the list actively work with the FATF to address strategic deficiencies in their system to fight money laundering, terrorist financing, and other financial crimes. Indeed, the UAE has made a high-level commitment to work with the FATF to strengthen the effectiveness of its AML/CFT system and resolve the identified strategic deficiencies within agreed timeframes under tighter supervision. The FAFT is expecting an improvement in areas such as the number and quality of STRs; greater use of financial intelligence to pursue high-risk ML threats; and proactively identifying and combating sanctions evasion, including by demonstrating a better understanding of sanctions evasion. As part of this effort, the UAE is also proposing to establish a national committee to allow sharing of strategic information and intelligence between the public and private sectors on suspected money laundering and terrorist financing activities, as well as develop best practices to fight financial crime. On a recent visit to the region, I met with new customers and prospects to discuss how to use AI technology as a tool to help solve their AML challenges and better position them to transform into significant players in the regional and global financial market and growing fintech ecosystem. Here are my impressions on how Middle East and North Africa FIs can benefit from adopting AI: Enable trust. Middle East banks and fintech are looking to grow business and boost connectivity to the global financial system. As noted, they are faced with unique challenges due to their relative proximity to risky areas, exposing them to manipulation by sanctioned entities and terrorist groups using front non-listed entities. By adopting AI-based solutions they are going to be better positioned to deliver effective and efficient AML programs. Using AI, true criminal financial activity can be detected in a timely

Embrace network visualization tools for more effective AML compliance in an increasingly complex financial world By Dagan Osovlansky, ThetaRay Chief Product Officer Fintechs and banks are struggling today to operate effective and efficient anti-money laundering (AML) and combating the financing of terrorism (CFT) programs, especially with the complexity of cross-border payments driven by new online platforms.  As digital payment velocity increases, AML compliance managers are exposed to enormous volumes of data.   To unravel complexity, big data analytics are becoming more essential for tracking money-laundering activities. Bad actors continuously develop new modus operandi, changing criminal schemes faster than investigators can get on the trail of crime. Indeed, today, the FATF is promoting the use of technology to implement a risk-based approach that can improve AML/CFT efforts. Specifically, according to the FATF, artificial intelligence-based tools can analyze data accurately and help better identify emerging risks. [add source]. Moreover, according to the FATF, technology has the potential to make efforts to combat money laundering and terrorist financing (AML/CFT) faster, cheaper, and more efficient. Big data tools are also evolving alongside sophisticated machine learning.  Technology can increase the capacity to collect and process data, and share it with stakeholders, including supervisors, notes the FATF. Data visualization provides a powerful tool for AML professionals to gain insights to be able to effectively analyze and communicate data. Here are some benefits of visualization tools for AI-powered AML transaction monitoring: Maximize the power of AI-generated data. AI-powered technology for AML, especially “unsupervised” machine learning, learns the financial behavior of each customer, builds “normal” profiles, and detects unusual cases. In this way, hidden risks and “unknown unknown” typologies can be uncovered within the data. When the AI-processed data is analyzed and then visualized, the dots are connected, and discoveries are enriched, shedding new light onto abnormalities and enabling uncovering of new typologies in an increasingly complex financial world. See the big picture. Visualizations are more intuitive than text alone, and can therefore help provide context for red flags. Additionally, visualization can expose “hidden” suspicious patterns that cannot be revealed by alerts and/or transaction tabular representation. Using a visually mapping out financial network, AML compliance teams can thereby more easily explore relationships among different players, opening up the layers and revealing the source of activity from one to the next. Enriching customer data with visualization helps deliver more accurate risk assessments, better decision-making, and fewer instances of false alerts. Speed investigations. The big picture enables faster and smarter decisions. Having network visualization as part of a machine learning ecosystem improves the investigation process, enabling faster investigations through clear and intuitive visualization of activity on financial networks. Layers of the data behind alerts can be presented and analyzed graphically, including recipient names, the volume of transactions, payment timeline, aggregation of currencies, transaction direction, and country of origin. When transactions can be easily and clearly viewed transactions across the networks, analysts and supervisors can see hundreds and thousands of transactions at one glance including interconnectivity with other entities.  As a result, significant time and operating