Vered Gottesman, CMO at ThetaRay, and Nitzan Solomon, VP of Product at ThetaRay talk about our latest product innovation: ThetaRay Customer Risk Assessment.

In this evolving world of financial crime detection where AI and ML play a pivotal role in unraveling the complexities of illicit activities, there is a debate and perhaps confusion over which technique should be applied to best resolve the problem. Among the arsenal of ML techniques, two prominent methods stand out: unsupervised and supervised learning.

Scrutiny of the anti-money laundering (AML) and counter-terrorist fundraising (CTF) practices of Canadian banks and institutions has many questioning whether enough is being done to defend against current threats. Recent non-compliance findings by the Financial Transactions and Reports Analysis Centre of Canada (FINTRAC) imply there is room for the private sector to do better. In December 2023, FINTRAC levied its biggest penalty to date against the Royal Bank of Canada (RBC), fining RBC C$7.5 million for failing to submit suspicious activity reports (SARs). FINTRAC then issued another fine against the Canadian Imperial Bank of Commerce (CIBC), penalizing them almost C$1 million dollar for non-compliance with AML and CTF measures. It’s important to note, that these fines are not penalties for actual criminal offenses; they are penalties for failures in the bank’s controls and governance. However, these failures represent potential weaknesses in the defenses of the institutions. These failures indicate that criminal activity could be going undetected and that these institutions are highly susceptible to criminal activity in the future. AML and CTF non-compliance is not new These dangerous lapses in compliance, unfortunately, have been part of the Canadian landscape for some time. A follow-up to a 2021 report by the Financial Action Task Force (FATF) found weaknesses in the regulation, enforcement, and effectiveness of Canada’s financial intelligence unit (FIU), FINTRAC. The report assessed FINTRAC as only “partially compliant”, only one step higher than the lowest grade – “non-compliant”. Some analysts attribute FINTRAC’s recent activity as an indication they have taken these criticisms to heart and are focused on improving the compliance vigilance of the institutions under their watch. As a result, Canadian banks and institutions are, in turn, looking at their programs and taking steps to bolster their AML and CTF compliance to reduce their risks and improve the overall strength of their capabilities. The time is now for banks to strengthen AML and CTF The Canadian financial system, due in part to the aforementioned lapses in AML and CTF compliance and its proximity to the U.S. market, is very attractive for bad actors looking for a place to hide their money and fund their criminal activity. As a modern, democratic, open country, Canada consistently ranks high on the index of economic freedom. However, the very institutions and principals that protect the rights of individuals to pursue their own economic interests can also be exploited by criminals for their own ill intent. These bad actors try to use Canadian banks as a final destination for their illicit funds, as well as for layering. It is advantageous to conceal the origin of their funds through a series of transactions making it seem make it seem as if the money is coming from Canada. According to Global Regulatory Insights (GRI) money laundering offenses in Canada are linked to a wide variety of criminal conduct, including drug trafficking, fraud, corruption, human trafficking, organized crime, and terrorism.  In addition, FINTRAC has uncovered activity related to “homegrown terrorism, the bankrolling of international terrorist groups, and

Remittances are an essential way to bring money into low-income countries and unbanked communities, but they are all-too-frequently used as a conduit for financial crime. By nature, money transfer and remittance are high-risk businesses.  Therefore, to ensure that financial crime is detected, banks and fintechs traditionally use rules to monitor transactions which may catch some crime but also result in a significant number of false positives and manual reviews. Adding to the challenge, remittance is a low-margin business requiring speedy transactions which means that providers aren’t constantly trying to reduce the need for costly manual reviews while remaining compliant – a zero-sum game using rules-based systems.  As remittance service providers scale their businesses to handle larger transaction volumes, they must adopt highly automated solutions to replace rules-based monitoring solutions to keep up with evolving compliance demands and deliver trusted, swift services.  The goal is to detect potential risks and threats reliably and effectively without compromising the quality of service. Only when they can reduce costs and capture efficiencies can remittance providers compete against other, lower-cost money transfer businesses while remaining secure.  A long list of challenges  Today, remittance service providers face increasingly high hurdles, including: Managing AML/counterterrorism financing (CTF) risk in the face of increasingly complex financial crime typologies. Keeping pace with global compliance requirements and increasing pressure to contain risks and coordinate efforts across multiple jurisdictions, increasing costs and time pressures for international payments.  Managing requirements across partner relationships and ensuring that partnership agreements don’t compromise service delivery guarantees. Accommodating crypto and digital currencies while meeting Know Your Customer (KYC) requirements.   These obstacles mount when remittance providers use legacy regulatory and financial crime risk management solutions, which struggle to identify many threats, often fail to capture the correct data to act quickly, and fall short in mitigating losses and avoiding regulatory breaches. Only financial crime-fighting technology powered by artificial intelligence (AI) can ensure easy, efficient, and trusted transactions worldwide.   The benefits of automated, AI-driven solutions include: Serve customers with speed at low cost: Remittance providers can’t afford to spend hours reviewing transactions because customers are always looking for the cheapest, fastest way to send money. With AI-powered AML, remittance providers can surpass competitors by quickly identifying risks and reducing operating costs. Detect unknown cases: Transaction monitoring systems are somewhat effective in identifying “known risks,” but they offer little in helping proactively mitigate “unknown risks.” By leveraging unbiased and semi-biased AI, financial institutions can recognize anomalies and find unknowns outside normal behavior, including entirely new typologies.  Expand risk coverage: With rules-based systems, remittance providers are limited in their risk coverage while risk-based AI systems are far more effective and efficient. Grow their businesses: With an AI-powered AML system in place,  100% of red flag coverage for all known money-laundering risks are detected using a multi-layered solution, including unsupervised, semi-supervised, and rules in cooperation. Identifying risks more accurately and precisely enables you to grow business with partners in high-risk countries and segments.  Boost operational efficiency: By using AI remittance providers can

CMO Vered Gottesman asks CPO Dagan Osovlansky about ThetaRay’s new CRA product Vered: ThetaRay just launched CRA – Customer Risk Assessment.  What differentiates ThetaRay’s CRA from existing customer due diligence products on the market? Dagan: Customer due diligence (CDD) requires a comprehensive process covering multiple aspects of understanding the customer. ThetaRay’s CRA product focuses on the risk assessment aspect as part of this entire KYC (Know Your Customer) process. With our new CRA product, ThetaRay conducts a continuous risk assessment throughout the full customer lifetime, not just during onboarding.  Unlike traditional CDD solutions, which often rely on broad classifications and stereotypes, ThetaRay’s CRA product takes a deeper dive into who your customer is by assessing risks on an individual basis. This provides a more accurate and fair picture of each customer throughout their lifecycle. ThetaRay does this by taking data from both demographic/static sources as well as from current and historical transactions. We can assess the risk of a particular individual based on behavior, not just demographic information. So while it is true that certain static data points such as country of residence might be more correlated with financial crime, insights from actual behavior including transactions are more accurate.  ThetaRay’s CRA is fully automated and uses machine learning, which is very different from the rules-based systems available on the market today. With rules-based systems, you set certain criteria and give weight to whether or not the criteria were met and so on. The problem with this is the amount of data and indicators that can be included are very limited. Furthermore, current transaction and behavioral data is not even taken into account.  V: CRA allows you to configure the criteria and cadence of risk assessment throughout the customer journey. How does that work? D: We enable our customers to add criteria or change the weight of criteria, which gives them flexibility.  In addition, CRA allows for cadence calibration. Most due diligence cadences take place once a year for risky customers, or once every two or three years for low-risk customers. While more advanced solutions can trigger an assessment each time your KYC data changes, such as when you move addresses, it’s still based on the same time-limited capabilities.  To truly prevent bad actors from infiltrating banks, one needs to assess risk almost constantly. Thanks to the dynamic, automated process CRA offers, ThetaRay detects high-risk customers much faster compared to three-year-old KYC information that only tells a partial picture at best.  Furthermore, CRA’s superior accuracy means you’ll actually reduce the number of customers categorized as high-risk. That’s because during onboarding, a customer is assessed but only from existing demographic and other static data. As the customer begins to execute transactions every day or week, ThetaRay can assess customers based on behavior and adjust their risk. For example, maybe a customer’s geographic location has a significant amount of financial crime. However, when you see how the customer behaves and that his or her behavior matches the information in the KYC, it becomes

Top Five Things Organizations Can Do to Modernize Their Approach to Addressing Financial Crime By Vered Gottesman, ThetaRay CMO