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

The mounting challenge of trade for AML compliance Trade financing is a big business for banks, with letters of credit and an important revenue stream. For the developing world, global trade is key to economic growth. The global trade finance market, with an estimated value of $5.2 trillion, facilitates the cross-border movement of goods and services around the world. It includes buyers and suppliers of all sizes, with financial institutions providing the liquidity and the risk assessment necessary to execute trade transactions. At the same time, the Financial Action Task Force (FATF), a global financial crime watchdog, has identified trade as one of most worrisome methods by which criminal organizations and terrorist financiers move large amounts of money while disguising origins and integrating the funds into financial systems. A growing global problem in the past several years, international trade attracts bad actors and terrorists aiming to exploit legitimate types of activities while manipulating and misrepresenting the quality, value, and or quantity of imports and exports. Indeed, trade-based money laundering (TBML) is one of the most sophisticated methods of “cleaning” dirty money, notably for narcotics traffickers and terror organizations. Due to the complexity of trade finance and global shipping logistics, money-laundering red flags are usually manyfold and also among the hardest to detect. While the scope of money laundered through the international trade system is also unknown, as estimates range vastly from hundreds of billions to trillions of dollars per year.  As a result, many banks often choose to de-risk or limit activity, meaning businesses and economies lose access to global trade. To help address the challenges of detecting trade-based money laundering, the FATF has asked financial institutions to redouble efforts and reassess controls to ensure they are using the right tools in combatting TBML. In March 2021, a new guidance issued specific guidelines for trade-based money-laundering risk factors. Adopting a risk-based approach  Classic money-laundering flags used in a rules-based approach, such as threshold values and high-risk jurisdictions, are simply too general for the complexity of global trade where so many factors are involved in the transaction.    The FATF highlighted a host of risk indicators that can point to TBML in the guidance. The multitude of risks is grouped as follows: Structural risk indicators such as high-risk jurisdictions, suspected use of shell companies, a lack of public information or corporate offices, little or no business activity, and criminal records.  Trade activity risk indicators include a discrepancy between the line of business and the trade activity, use of many intermediaries, abnormal shipping routes, unconventional use of letters of credit, importing wholesale commodities at or above retail value unreasonably low-profit margins, a newly formed or recently re-activated trade entity engage in high-volume and high-value trade activity.  Trade document and commodity risk indicators for example inconsistencies across contracts, invoices, or other trade documents or prices that do not seem to be in line with commercial considerations. For example, when the value of registered imports of an entity displays significant mismatches to the entity’s volume of foreign

Nigerian fintech selects SONAR SaaS solution for AML and sanctions screening to create new revenue opportunities and boost customer service. ThetaRay, a leading provider of AI-powered transaction monitoring technology, today announced that ARCA, a premier African payment services provider, will implement ThetaRay’s advanced SONAR SaaS anti-money laundering (AML) and sanctions list screening solution for transactions on its open AI-based platform. ARCA is the first Nigerian fintech to adopt ThetaRay’s advanced SONAR solution, industry renowned for its ability to detect the very first signs of sophisticated financial crime. ARCA provides advanced digital payments for an open banking ecosystem, helping expand innovative and inclusive financial services throughout Africa. “Our mission is to provide feature-rich financial solutions delivered through an open and flexible digital platform, through the use of cutting-edge technologies,” said Alex Umeh, Chief Information Security Officer at ARCA. “ThetaRay’s SONAR is a perfect fit. Its advanced machine learning and algorithms can instantly spot any attempts to launder money or circumvent sanctions, no matter how sophisticated. This will help us to create new lines of revenue, better serve our customers, and continue to remain compliant with regulatory requirements.” “Instant payments have become the new norm in the digital ecosystem, and ARCA is a leader in driving this revolution in the African financial system,” said Mark Gazit, CEO of ThetaRay. “ARCA prioritizes trust, confidence, and quality. We are thrilled to build this partnership and help facilitate both the growth of their business and expansion of the world economy by enabling financial inclusion.” SONAR is based on an advanced form of AI that makes better decisions with no bias or thresholds. It enables fintechs and banks to implement a risk-based approach to effectively identify truly suspicious activity and create a full picture of customer identities, including across complex, cross-border transaction paths. This enables the rapid discovery of both known and unknown money laundering threats, with a 99% reduction in false positives compared to rules-based solutions. About ARCA ARCA was founded in 2016 with a clear vision to become Africa’s premier payment services platform, fostering financial inclusion and innovation, and actively shaping the future of financial services throughout the region. The ARCA system empowers banks, financial institutions, developers & SMEs to seamlessly connect and create solutions for their customers. Learn more at www.arca.network. About ThetaRay ThetaRay’s AI-powered SONAR transaction monitoring solution, based on “artificial intelligence intuition,” allows banks and fintechs to expand their business opportunities and grow revenues through trusted and reliable cross-border payments. The groundbreaking solution also improves customer satisfaction, reduces compliance costs, and increases risk coverage.  ThetaRay’s technology is the only SaaS offering that analyzes SWIFT traffic, risk indicators and client/payer/payee data to detect anomalies indicating money laundering activity across complex, cross-border transaction paths in a single unified platform. Financial organizations that rely on highly heterogeneous and complex ecosystems benefit greatly from ThetaRay’s unmatchable low false positive and high detection rates. Learn more at www.thetaray.com. Itweb: https://www.itweb.co.za/content/G98YdMLGYkd7X2PD Africa business communities: https://africabusinesscommunities.com/tech/tech-news/thetaray-ai-tech-to-monitor-african-payments-for-nigerian-fintech-arca/   Linkedin

The Latin American payment fintech market is growing rapidly with new digital platforms fulfilling the needs of underserved financial consumers in the region. The region is enjoying phenomenal growth, with the number of firms reaching nearly 2500 in 2021 from 700 five years ago, according to data from Finnovista. Brazil and Mexico alone host a combined 50% of new fintech, followed by Colombia and Argentina. The opening of LATAM’s financial markets is a huge business opportunity. But it is also creating a challenge for regional and global regulators to ensure compliance with international AML standards. Therefore, having the right AML solution is key to winning more business in a fast-growing market. On a recent visit to the region, I met with new customers to discuss their transformation into significant players in the financial market using advanced AI-based AML solutions to succeed in the new fintech ecosystem. Here are some takeaways from my discussions. The growing volume of transactions is a challenge for rules-based systems. As business grows in Latin America, FIs have seen exponential growth in the volume of transactions. When using rules-based legacy systems, the sheer quantity of false positives becomes an unbearable burden on compliance teams. Therefore, rules-based systems are practically a non-starter for fresh startups that will find quickly they cannot maintain these systems. Instead, fintechs are now looking for more innovative systems based on AI that can efficiently and effectively detect financial crime by pinpointing true cases of abnormalities while allowing the free flow of customer business. Using AI-based machine learning solutions for AML increases coverage continuously to maintain a high standard on the positive detection rate while reducing noise/false positives and subsequent operational costs and efforts. In addition, SaaS-based platforms offer the scalability and elasticity that can reduce future infrastructure costs, as well as ensure smooth performance and operations while maintaining optimal cost-benefit. The complexity of payment types and destinations Latam fintechs are handling a complete mix of cross-border B2B, remittances, and domestic transactions, with aggregation of payments in batches. In addition, AML requirements require real-time screening of both local payments or any mass/local payment that will originate a future cross-border consolidated transfer payment. This complexity requires a transaction monitoring solution that has the ability to differentiate between transfer typologies and entities and connect the dots across complex paths. AI-based AML can connect the dots across complex paths and transactions. It can put together a proxy of a client’s identity for compliance teams based on behavior patterns, even when KYC information is missing. In contrast to rules-based solutions, AI-based AML analyzes data dynamically without relying on predefined scenarios or models, a must today with the high prevalence of new and emerging money-laundering patterns and other schemes. Getting a firm grip on managing risk As fitechs in the region work to integrate into the global ecosystem, pressure from regulators is mounting. Foreign exchange and global payment remitters alike are under pressure to maintain a strong compliance hold of all transactions in the country, and even domestic

Next-generation transaction monitoring tools that use AI and ML technologies are getting faster and more accurate, drastically cutting the number of false positives. Yet fear and suspicion around the “black box” nature of AI is still keeping potential adopters away from these technologies. Black box AI refers to a problem in machine learning whereby it is difficult to explain how conclusions were reached – sometimes even by the algorithm designers themselves. This presents a huge hurdle in a stringent regulatory world where influential bodies such as the Monetary Authority of Singapore (MAS) are calling for fairness, ethics, accountability, and traceability (aka. FEAT) when using AI in the financial sector. In today’s climate, transparency is critical, and regulators must be able to determine if results produced by AI transaction monitoring software were influenced by bias or prejudice and that the correct datasets were used. Fintechs must be able to explain their past screenings transparently and comprehensively to any external auditors, but many of the AI-based screening tools in use today make this impossible.  What the industry really needs (and what very few are able to provide) is a “glass box” model with a highly transparent path from data, through the process, to output and in which it is possible to not only explain why certain results occurred but also how the algorithm got there. The Tides are Turning    There is no stopping the march of progress. Fears around black box AI notwithstanding, regulators, analysts, and big players in the financial services industry are promoting AI’s potential to upgrade the effectiveness and efficiency of AML transaction monitoring on multiple levels. In fact, FATF (The Financial Action Task Force) strongly recommended the usage of AI and machine learning for AML and CFT detection, and also recommends the following: Move away from a rules-based approach – With their abundance of false positives and excessive reliance on human input to create underlying rules, these tools are too burdensome and expensive to run, slow to adapt to new realities, and often not sensitive enough to pick up sophisticated or innovative crimes. Adoption of AI transaction monitoring tools by regulators – The high volume of data generated by fintechs and other financial institutions makes the task of the regulatory bodies virtually impossible without newer tools that are more fit for purpose.  Greater use of AI to enhance financial inclusion – AI transaction monitoring is more sensitive and can spot suspicious activities that rules-based systems have been historically unable to intercept. This means that instead of blacklisting entire countries deemed to be too risky, these countries can now be invited back into the world’s financial fold, knowing we have the right tools to provide adequate security, Citizens of these nations will then be able to benefit from conveniences such as online payments and mobile banking which will help break the cycle of poverty. What is Glass Box AI (And is it Too Good to be True)? With regulators actively promoting AI transaction monitoring software, these tools are

Fintechs rely on transaction monitoring software to secure their innovative platforms against increasingly sophisticated financial crimes. They have an obligation both to their customers and to regulators to ensure their financial business is not exploited by criminals. But in the fast-moving and agile world of fintech, they can’t afford to adopt software solutions that slow down the rate of transactions.   With the constant advances in technology and an active and creative enemy, fintech must adopt cutting-edge systems that enable them to reliably and quickly detect, intercept and report any suspicious activities.  The best transaction monitoring software will cope with a rapidly evolving and increasingly complex environment and keep legitimate payments of ordinary customers flowing when adopting the most advanced payment technologies.   Here are 4 key criteria to keep in mind when choosing your AML transaction monitoring software. Regulatory Compliance According to Deloitte, “financial service providers are investing increasingly in intelligent solutions that use modern technologies to reduce the costs of regulatory compliance.” The rapid uptake of AI-based transaction monitoring software by Tier 1  banks is a clear sign of this trend. Regulators are also lauding the benefits of AI AML, with FATF (The Financial Action Task Force) strongly recommending the use of AI and machine learning for AML and CFT detection. This means that a path toward better AML has already been laid.  Adopting AI AML tools is not a legal requirement, but regulators are becoming stricter yearly with compliance requirements. It seems inevitable that fintechs will be under pressure to adopt the highest levels of monitoring in the not-too-distant future. Furthermore, using AI transaction monitoring tools can help prevent AML compliance fines.  Rules-based vs. AI in transaction Monitoring In recent years, FATF has called for a risk-based approach to preventing money laundering.  This means that financial institutions and businesses must “identify, assess, and understand the money laundering and terrorist financing risk to which they are exposed, and take the appropriate mitigation measures in accordance with the level of risk.” In practice, this means that not all customers or jurisdictions are to be treated equally from a risk point of view.  Customers or countries known to be at a higher risk will therefore benefit from more stringent levels of monitoring. The problem lies in defining who is at risk and what stringencies to apply.  Rules-based,  traditional transaction monitoring software means that rules are used to determine which transactions require further investigation. In rule-based systems, the rules must be predetermined and programmed upfront, and it is easy to be either too comprehensive (resulting in too many false positives) or not comprehensive enough (resulting in suspicious activity going undetected). A new breed of transaction monitoring systems using AI and machine learning, on the other hand, can analyze a staggering array of factors within datasets spotting patterns and anomalies without relying on human input. These systems can derive conclusions from the data alone, thereby identifying unknown patterns and connections. Because AI transaction monitoring software learns from the data itself, it operates more effectively