The future of sanctions compliance: moving beyond traditional screening

Key takeaways

• Traditional screening falls short – Name-based methods produce too many false positives and miss complex evasion tactics.

• AI enhances detection – AI and machine learning identify hidden patterns and risks in real time.

• Generative AI adds context – It analyzes news and unstructured data to improve accuracy.

• Unified compliance boosts results – Integrating sanctions, AML, and KYC improves risk detection.

• Data and collaboration matter – High-quality data and early regulator engagement are key to success.

Rethinking sanctions compliance

Sanctions compliance has long been a cornerstone of financial crime prevention. However, as the regulatory landscape evolves, and financial criminals become increasingly sophisticated, traditional sanctions screening methods are struggling to keep up. Historically, financial institutions (FIs) have relied heavily on name-based algorithms and sanctions lists to identify potential risks. While this approach continues to be the de facto requirement, FIs are now facing significant challenges in the face of more complex evasion typologies. This article explores why traditional sanctions screening is no longer enough, how technology is transforming sanctions compliance, and why financial institutions need to rethink their approach.

The limitations of traditional sanctions screening

Traditional sanctions screening relies primarily on name-based algorithms and the use of publicly available sanctions lists (such as those from the U.S. Treasury Department’s Office of Foreign Assets Control, or OFAC). While this approach may seem straightforward, it is riddled with limitations.

One of the biggest issues with traditional screening is the high rate of false positives. Sanctioned parties often pivot their behavior after being publicly listed, making screening a good “day one control,” but leaving very little likelihood of true hits beyond a short window. According to a 2020 study by the Bank Policy Institute, traditional screening methods yielded minimal true matches, often around zero, while generating a significant number of false positives. This not only wastes valuable resources but also diverts attention away from real risks.

Furthermore, traditional screening fails to account for more complex behaviors and financial crimes, such as sanctions evasion, which is not always detectable through basic name-based matching. As criminals adapt to evade detection, these outdated methods are increasingly ineffective.

The need for technology-driven solutions

With the limitations of traditional screening becoming more apparent, the financial services industry is turning to the latest technology innovation to enhance sanctions compliance programs. AI, machine learning, and big data analytics are revolutionizing the way institutions search for sanctions risks. These technologies offer a more sophisticated, dynamic approach to risk detection, allowing FIs to move beyond simple name-based matching and identify patterns and behaviors that indicate potential sanctions evasion.

AI-powered risk detection

AI is rapidly gaining traction in the field of sanctions compliance. By utilizing machine learning algorithms, AI can analyze vast amounts of data in real-time, identifying patterns and anomalies that human compliance officers might miss. AI-driven systems can track transactions, cross-reference customer data, perform research, and even detect subtle changes in behavior that may suggest the possibility of sanctions evasion. And it’s not just investigative teams that stand to benefit. Sanctions advisory teams also can benefit by using AI to perform research and drive consistency in the guidance they provide.

For example, AI-powered systems can recognize complex financial networks or suspicious transaction patterns, such as the use of intermediaries or shell companies, which are often employed by individuals or organizations trying to circumvent sanctions. This allows FIs to detect and prevent potential violations earlier, enhancing the overall efficacy of their sanctions compliance programs.

Generative AI for contextual insights

Another key technological advancement is generative AI, which is capable of analyzing unstructured data such as news articles, reports, and social media posts. This type of AI can provide valuable contextual insights that enhance the screening process. For instance, generative AI can offer additional information about an entity’s background, including any affiliations or activities that may not be immediately apparent from transaction data alone.

By combining predictive and generative AI, FIs can significantly improve the accuracy of their sanctions screening, reducing false positives and ensuring true positives are not missed.

Convergence of sanctions, AML, and KYC

One of the biggest challenges in sanctions compliance is the siloed nature of compliance functions within Fls. Sanctions compliance, anti-money laundering (AML), and know-your-customer (KYC) functions are often managed separately, leading to inefficiencies and gaps in the risk detection process.

However, as financial crimes become more intertwined, it is becoming increasingly important to break down these silos and integrate sanctions compliance with other key areas of financial crime prevention. By adopting a more holistic, unified approach to compliance, institutions can better detect and address risks that span across multiple domains.

For example, sanctions evasion typologies often look similar to traditional money laundering typologies typically reported on suspicious activity/transaction reports. In addition, KYC data and risk assessment is a crucial data source to assess customer/counterparty risk across different compliance functions. By integrating AML and KYC data into sanctions compliance systems, FIs can create a more comprehensive risk profile, improving the chances of identifying hidden, complex evasion risks.

Improving data quality for better screening

In addition to leveraging advanced technologies, another key element in improving sanctions compliance is the quality of the data being screened. Outdated or incomplete data can result in missed matches and failed risk detection. Financial institutions must ensure that the data they use for screening is accurate, up-to-date, and comprehensive.

This involves improving data integration across various compliance systems and ensuring that all relevant customer information, transaction data, and external sources (such as news feeds, watch lists, and adverse media reports) are in scope when screening for potential risks.

The role of education and collaboration

As new technology emerges at lightning speed, it is important for FIs to familiarize themselves to determine the right solutions and ease the adoption process. Similarly, regulators have their hands full trying to get up to speed with the latest technology. Therefore, it is crucial for FIs that are looking to adopt the latest technology to engage with regulators early and often. In the long-run, this can help make regulators more comfortable with the technology at an industry-level, paving the way for a more welcoming regulatory climate.

Conclusion

Effective sanctions compliance is no longer a matter of simply checking the screening box. With the rise of increasingly complex evasion typologies and the growing pressure to detect evasion, FIs must embrace new technologies to ensure compliance and effective risk management. AI, machine learning, and data analytics are transforming the approach to sanctions compliance.

By integrating these technologies, improving data quality, and breaking down silos between compliance functions, FIs can enhance their ability to detect sanctions violations and mitigate the risks associated with non-compliance. The future of sanctions compliance is here, and it’s time for institutions to innovate and adapt.