In banking and financial services (BFS), battling the increasing volume of illicit activities and sophisticated bad actors can seem relentless (and perhaps daunting). While bad actors are constantly innovating, the same can’t be said of financial institutions. There has been an unsustainable status quo within anti-money laundering (AML) and sanctions compliance. There are financial crime problems today that existed 15-20 years ago, and BFS have been slow in finding innovative ways to solve them. We’ve had decades of doing the same, and we need to break that cycle.

Fortunately, Artificial Intelligence (AI) is emerging as a potent ally, reshaping and transforming financial crime compliance efforts by automating many labor-intensive but necessary processes, such as reviewing AML and sanctions screening alerts. Currently, 70% of banks face capacity challenges in their compliance operations. That, combined with the surging costs of compliance efforts and fines, creates the perfect storm. Consider this: the global cost of compliance for banks was nearly $275bn in 2022, with a significant portion, 60%, tied to direct and outsourced labour. And fines for failing to meet AML compliance can often reach millions of dollars, according to the Association of Certified Anti-Money Laundering Specialists (ACAMS).

Innovative financial institutions are taking notice. In fact, 78% of financial institutions are looking to use technology to automate processes and improve effectiveness and efficiencies, according to the recent Technology Transformation in Financial Crime Compliance report.

Here are five ways AI is transforming financial crime compliance.

Alleviating sanctions strain: finding the needle in the haystack with accuracy

Sanctions compliance puts an enormous and growing burden on banking and financial services companies. Staying current on evolving sanctions is challenging and involves spikes in the alert volumes needing review, such as those related to the Russia-Ukraine war.

The problem starts with the rules-based sanctions screening software. These generate large numbers of sanctions alerts given the conservative thresholds being used, and unfortunately for most financial institutions, 99% of those alerts end up being false positives. Still, an employee must review each alert manually to discover the tiny percentage of potential true positives that pose a risk to the organisation. It’s finding the needle in an enormous haystack.

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AI technologies, including advanced language models, can greatly improve data accuracy. Nearly all the Office of Foreign Assets Control (OFAC) violations and Voluntary Self-Disclosures I have witnessed were because of human mistakes. By automating data extraction, processing, and analysis, AI can find errors that are hard for humans to see or overlook due to a false positive conditioning. This is critical for accurate and comprehensive audit trails required for compliance.

Enforcing payment compliance in real-time

As the adoption of real-time payments (RTP) accelerates, financial institutions must meet both transaction timing and sanctions screening requirements, creating a double burden that is diametrical. Also, cross-border payments are exceedingly more complex since they involve bridging multiple currency systems and regulatory jurisdictions and generate far more sanctions alerts.

The increased potential for financial crime and sanctions evasion with cross-border RTP has attracted the attention of regulators. OFAC has brought several enforcement actions on financial institutions and fintechs that violated sanctions compliance controls, specifically related to their failure to use geolocation tools. Regulators, including OFAC, recommend developing and deploying innovative sanctions compliance approaches and technologies to address the risks. This is where AI comes in. AI can assess and analyse vast amounts of financial transactions in real-time, identifying patterns and anomalies indicative of potential non-compliance.

Fueling the transition from Know Your Customer (KYC) to Perpetual Know Your Customer (pKYC)

Banks need to know who their customers are and what they’re doing and capture and apply that information. A client may look great when they walk through the door, but things are constantly changing, and depending on the refresh cadence, it can take years using KYC practices to identify that a customer’s risk level has changed.

Perpetual KYC or pKYC, powered by AI, introduces a paradigm shift to speed up both monitoring and event reaction. AI can manage information overload, bring in different risk aspects, make sense of the myriad of data points, and analyse if the risk is material. With pKYC, organisations can better allocate resources to manage and mitigate risk, especially for higher-risk customers, onboard more customers, and eventually move KYC from a cost center to a revenue generator.

Automating labour intensive processes to overcome staffing challenges

One of the most pressing challenges financial institutions face in combating financial crime is the labour-intensive nature of current practices to review vast amounts of diverse data with insufficient headcount. Organisations continue to struggle to find candidates. We have heard from several customers that they’ve had open AML and Sanctions positions for months.

Once you find someone for an open role, you must onboard and train new analysts and, in many cases, re-train, which can take months. This comes with risks like backlogs, overworked staff, SLA delays and errors, missed escalations, and possible remediation efforts — with all of these putting the programmes effectiveness at risk

Turning to AI can empower financial institutions to reduce manual (and repetitive) work so teams can focus on higher-risk, higher-value, more fulfilling investigative work, and risk analysis.

AI excels in processing and deriving insights from unstructured data. Whether it’s analysing textual information, documents, or media sources, AI technologies can efficiently handle vast amounts of diverse data. The ability to “hire AI” that works 24/7/365 can be a game changer for financial crime programmes.

Addressing financial crime prevention proactively and improving risk assessments

AI is helping organisations move financial crime prevention from a reactive to a proactive stance. AI for predictive analytics uses historical data and current trends to forecast potential financial crime activities and can help banks identify potential risks and vulnerabilities in real-time.

Additionally, AI can enhance risk mitigation efforts. This aligns with the risk-based approach the Financial Action Task Force (FATF) advocates. Traditional risk assessment methods often fail to provide a nuanced understanding of each customer’s risk profile. However, AI, can analyse a multitude of factors to offer a more accurate and detailed risk assessment. This allows financial institutions to allocate resources more effectively, focusing on high-risk customers.

While improving financial crime compliance operations is a big step for most financial institutions, you don’t have to start big — with big price tags or heavy implementations. To begin, turning to AI can help automate much of the work for Level 1 analysts, for example, in sanctions and AML compliance, reduce the noise of false positives, and identify true material AML risk. Incorporating AI in the right ways — with tailored solutions to align with your unique requirements — can help you stay resilient and effective in the fight against illicit activities.

Arthur Mueller is VP of Financial Crime, WorkFusion