Niall Twomey, Chief Product & Technology Officer, Fenergo.
The most recent geopolitical turmoil, fueled by the financing of terrorism, has brought to light the real-world impact of financial crime and the need to prevent money laundering. The repercussions for the people and financial institutions involved can be detrimental—and regulators are actively addressing these issues.
In the recently released annual Examination Priorities, the Securities and Exchange Commission (SEC) elevated money laundering as a top risk area for 2024, calling out customer due diligence and beneficial ownership compliance in particular.
Several major banks were on the receiving end of enforcement action from regulators in 2023, and the recent penalty against Binance highlighted the severe consequences of compliance failures. Banking compliance leaders are facing changing regulatory frameworks and scrutiny, economic uncertainty, growing geopolitical risks and the unprecedented sophistication of financial criminals.
Artificial intelligence (AI) and machine learning (ML) powered compliance solutions play an indispensable role in helping banking compliance leaders battle financial crime to build operational resilience, prevent regulatory fines, fortify the bank’s reputation and increase profit margins.
Sophisticated Criminal Behavior
Financial criminals are highly motivated to innovate and are quick to probe cracks in the financial system, developing more advanced ways of layering and integrating how they’re committing financial crimes. They move money through cryptocurrency exchanges, over-the-counter brokers, gambling companies, peer-to-peer networks and even legitimate corporations.
Even if banks deploy algorithms that alert specific criminal activity, bad actors have become agile in pivoting to different behaviors. Without AI/ML tools, banks may be too slow to recognize changing criminal behavior. It is not uncommon for months to speed by before legacy processes identify trending behavior. An effective anti-financial crime system must be able to go beyond set-configured scenario rules.
AI Transaction Monitoring To Alert And Remediate
AI technology is essential to gathering “financial intelligence” and recognizing these changing patterns and evolving trends. What was once a straightforward albeit slow manual function in the analog world now requires data automation to parse through galaxies of transaction data to determine what is and isn’t illicit activity.
Financial institutions (FIs) can now employ AI-powered anti-money laundering (AML) transaction monitoring (TM) tools to spot trends and modify algorithms and policies in real time to adapt to new trends as they emerge. AI transaction monitoring tools go beyond a mere suspicious activity alert by remediating the alert, efficiently pulling in data from other sanctions lists, Know Your Customer (KYC) profiles and due diligence, revealing a full customer profile.
ML technology helps save time with automated risk assessments and risk prioritization, which is crucial for banks to remain vigilant. Without automation, compliance professionals can be overburdened with tedious low-level AML/KYC tasks, resulting in inefficient workflows, more false positives and an imbalanced focus on low-value criminal activity.
AI For KYC: Prevention Better Than The Cure
LexisNexis’s 2023 report revealed that KYC processes during account onboarding rank as the primary concern globally in compliance screening operations, followed by customer risk profiling. Banks with muscular KYC processes have the foundation from which all anti-financial crime efforts interconnect. More than anything else, preventing crime means knowing your customers and every touchpoint in their journey.
AI/ML improves KYC by optimizing the client onboarding process, beginning with data integration and orchestration. The technology is revolutionizing KYC workflows, enabling swift, automated change assessments, capacity management and confidence scoring.
AI is executing tasks with speed and accuracy humans cannot duplicate, from risk impact/scenario and regulation interpretation to real-time compliance analysis and advanced reporting. Meanwhile, compliance professionals are also turning to these “AI copilots” for tasks like search, configuration and collaboration.
Automating Client Journeys
Institutions can leverage AI to enhance KYC and transaction monitoring, but it’s unwise to regard financial crime compliance technology as a suite of a la carte tools. AI/ML can (and should) be deployed to fortify anti-financial crime compliance throughout the client lifecycle. This includes verifying customer identity through KYC, where AI aids in validating identities and conducting liveness tests. It also plays a crucial role in automating and streamlining client onboarding processes, including the extraction of data from sanctions and politically exposed persons lists.
Automated compliance solutions assist humans by automating the analysis of colossal reams of data in the ongoing monitoring of customers, regulatory changes and transactions. Most importantly, the operational efficiencies produced by AI-powered compliance technology enable banks’ compliance teams to apply a risk-based approach to fighting financial crime, allowing low-to-medium-risk clients to be onboarded faster while prioritizing skilled resources for high-risk cases.
AI Adoption Roadblocks
Larger banks face challenges integrating static legacy systems with new software platforms. According to Fenergo’s recent study, “Compliance and risk officers said they are most challenged by incumbent transaction monitoring systems that produce high false positive rates, an inability to scale in response to increasing transaction volumes, and difficulty implementing new rules or adapting existing ones.”
The study also revealed that legacy architecture (44%) tops the list of challenges banks seek to address with tech investment. Small digital native FIs and fintech neo-banks should adopt AI as a core part of the operating model at inception. But larger banks with less sophisticated technology have found that working with disruptive fintechs is better than against them. As such, bank-fintech partnerships continue to increase.
Reputational ROI Of Automated Risk Management
LexisNexis found that 72% of global financial crime and compliance decision-makers employ analytics to enhance compliance procedures. In an era when regulatory agencies are aggressively expanding rules to protect consumers, this needs to be 100%.
Moreover, volatile geopolitical dynamics require banks to adapt to abrupt changes in sanctions and sanction-evading behaviors, like bad actors going through intermediary countries or companies. Automation can help banks operationalize changes to regulatory policy quickly. An unsullied reputation in banking is about blocking financial crime, achieving spotless compliance and providing top-flight customer experience.
AI For Operational Resilience
Eighty percent of financial crime and compliance decision-makers are looking to increase their operational resilience to set their organizations up to withstand disruptions, recover quickly from challenges, maintain continuity and ensure long-term sustainability. Financial institutions must modernize compliance tools to foster growth and attract and retain risk and compliance talent. It’s not a trend—it’s a necessity.
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