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How AI can address the global sanctions challenge

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By Parth Desai

Over the past decade, trade and economic sanctions have become an ever more popular tool of foreign policy in an increasingly uncertain geopolitical climate. Aside from country-specific sanctions, such as those against Iran, Russia, North Korea, etc, more targeted regulations focus upon particular businesses or individuals. As a result, national and international AML, Screening and Anti-Fraud obligations have increased in both scope and complexity. Today’s Sanctions regimes have arguably never been more challenging for organisations to ensure they remain compliant and have the required checking processes and procedures in place.

Costs of failure
Failure to comply with Sanctions and money laundering obligations, can result in severe financial and reputational costs. In 2018, a leading European bank was fined US $54 million while a number of other bank fines over the last decade have exceeded several billion dollars, including a Swiss bank being fined $329 million, a German bank $1.45 billion, and a Japanese bank $315 million.

This year, a British bank was fined $1.1bn jointly by US and UK regulators for sanctions and money-laundering control breaches. The reputational and brand damage can also carry significant commercial and revenue costs. 

The False Positive Challenge 
One significant consequence of today’s more complex compliance regimes, and the limitations of traditional static compliance technologies, is the substantial rise in false positive hits, which has placed considerable operational and cost burdens on all financial institutions. Of the alerts typically generated, less than 1% represent real financial crime cases, so, banks have to manually review, monitor and rule out the other 99% of hits. Many of the systems in the market today use a traditional and static rule-based approach – with limited abilities to assist compliance professionals in processing and checking the rising false positives in a constantly changing world. 

AI-Powered Approach 
In recent years, many organisations have discovered the significant benefits of AI-based systems in increasing the accuracy of detection and reducing overall compliance costs by drastically reducing false alerts.

Leveraging the AI disciplines of Natural Language Processing (NLP) and Machine Learning (ML), are key areas for increasing accuracy of financial crime detection, and the use of Self-Learning capabilities can significantly reduce the efforts to manually review the false alerts. This intelligent AI-driven dual approach can drastically cut compliance costs while delivering reputational protection across all payment processes and counter parties, with a dramatic reduction in manual effort.

An AI-powered approach can combine the benefits of NLP, Knowledge Based Systems, and Machine Learning, with powerful Self-Learning capabilities, and also providing full explanations for alert review and internal and regulatory audits. Financial institutions need to understand and be confident about the actions being taken by the AI-based solutions and to explain to the auditors and regulators all decisions taken.

At Pelican, we use just this AI Self-Learning approach, firstly to dramatically reduce the number of false positives, and then to understand and classify false positive alerts generated by third-party tools, giving detailed explanations for each decision made by the system. This allows compliance staff to resolve false positives much more quickly – reducing inefficiencies and freeing up valuable resources.

We have successfully demonstrated the powerful capabilities of PelicanSecure Sanctions Self-Learning, with  our technology being able to classify over 75% of the false positives for a large financial institution.  By using an AI-powered approach, banks can overcome today’s increasingly complex Sanctions challenges. Lowering costs, ensuring compliance, protecting reputations. As the global regulatory landscape becomes even more complex, there is no better time that now to review your existing Sanctions Screening processes.