How AI can address the global sanctions challenge
Today’s global sanctions regimes have arguably never been more challenging for organisations to ensure they remain compliant and have the required screening processes and procedures in place.
Over the past decade, trade and economic sanctions have become an ever more popular tool of foreign policy in an increasingly uncertain geo-political 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.
Failure to comply with sanctions and money laundering obligations, can result in severe financial and reputational costs. In recent years several banks in various territories have faced fines that have exceeded several billions of dollars, including a Swiss bank being fined $329 million, a German bank $1.45 billion, and a Japanese bank $315 million. Last year a British bank was fined $1.1 billion jointly by US and UK regulators for sanctions and money-laundering control breaches. The reputational and brand damage can of course also carry significant commercial and revenue costs.
Traditional technology limitations
One significant consequence of today’s more complex compliance regimes, and the limitations of traditional current 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. Crucially, the 4-Eye control, part of a market directive, is set to double the costs of compliance.
In recent years, many organisations looked to leverage the AI disciplines of Natural Language Processing (NLP) and Machine Learning (ML), as proven technologies to increase the accuracy of financial crime detection. The use of Machine -Learning capabilities can also significantly reduce the efforts to manually review any false alerts generated. This intelligent AI-driven dual approach can drastically cut compliance costs while delivering reputational protection across all payment processes and counterparties, with a dramatic reduction in manual effort.
An AI-powered approach can combine the benefits of NLP, knowledge based systems, with powerful ML 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.
Pelican uses just this AI self-learning approach, first 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. Pelican employs NLP, an AI technology that offers human-like intelligence and common sense to offer a far superior approach to sanctions screening. This allows compliance staff to resolve false positives much more quickly – reducing inefficiencies and freeing up valuable resources. Pelican has been able to achieve over 75% reduction in false positives at a large global bank.
By using an AI-powered approach, banks can overcome today’s increasingly complex Sanctions challenges. As the global regulatory landscape becomes even more complex, there is no better time that now to review your existing Sanctions Screening processes.