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Leveraging AI to Manage Financial Risk in Trade Operations

Leveraging AI to Manage Financial Risk in Trade Operations 1

By Ami Daniel, CEO & Co-Founder, Windward 

The intricate nature of global maritime trade creates ample opportunities for exploitation by bad actors who seek to perpetrate financial crimes such as fraud and trade-based money laundering (TBML). The United Nations Office on Drugs and Crime estimates that the amount of money laundered globally per year is between USD 800 billion to USD 2 trillion, or 2-5% of global GDP. When it comes to maritime-related financial risks, financial institutions tend to think primarily in terms of sanctions compliance. In reality, maritime risk management, specifically screening for deceptive shipping practices, is key to mitigating anti-money laundering schemes and fraudulent events.

While it is hard to quantify maritime-related transactions, considering the fact that the majority of the world’s traded goods are carried onboard vessels at some stage of the transaction, we can assume volumes are high. As maritime trade inevitably grows, financial institutions must move towards an all-encompassing risk approach and harness advanced maritime intelligence as part of their trade finance processes. By adopting this approach, institutions can better understand and evaluate risk exposure, beyond sanctions compliance.

The state of maritime data in trade finance

The latest OFAC and OFSI advisories called on financial institutions to understand the risks involved with maritime trade and the need for regulatory systems. Therefore, most banks already have some level of maritime screening tools in place. But financial institutions need to consider how to go beyond list matching and leverage AI as a tool to integrate deceptive shipping practices.

Why? Deceptive shipping practices can be used to facilitate illicit trade, and as new risk typologies are discovered the exposure to fraudulent events is bound to grow. Currently, the use of maritime data is limited to supporting monitoring tasks related to commodity trade finance, and as an additional source for investigations only once a transaction is flagged as a risk in Financial Crime Compliance (FCC) operations. Financial institutions have yet to implement deceptive shipping practices as a risk filter across all trade transactions, to proactively detect illicit trade.

Challenges faced by the industry

Without the complete analytic picture of what connects maritime trade to the corporate world and risk calculation of vessels, cargo, and their associated companies, financial institutions cannot be expected to achieve a complete liability assessment. The impact is a mounting problem of false positives, and when considering the sheer volume of transactions, the financial cost of false positives becomes staggeringly high. When there’s a deal at stake, false alerts can cause critical delays and compliance teams may need to ask for additional documents. The impact? Clients can decide to take their business elsewhere.

Financial institutions must pursue solutions that can effectively lower false positives without disrupting day-to-day business operations.

Applications for AI

As with the issues facing countless global industries, the most effective solution for holistic maritime risk analysis lies in savvy AI implementation. Some applications for AI include:

  • Documentary trade – Financial institutions are expected to comb through vast amounts of data to detect red flags. Without a holistic view of entities, containers, vessels, and behavior, banks can’t accurately screen their trade finance transactions as well as monitor them in real-time. Implementing maritime AI into trade screening solutions can provide more visibility into each deal, by flagging fraudulent events across all entities involved and ultimately decreasing the risk exposure. This is key to supporting safe exit management and preventing onboarding new business with exposure to maritime risk.
  • Open accounts – Open account transactions are used in around four-fifths of international trade. With nothing more than a SWIFT message and often no trade or shipping documents, screening for suspicious trade and specifically for deceptive shipping practices gets even more challenging. Screening SWIFT messages will require AI not only to extract and standardize the information, but screen all related parties mentioned in the transaction, as well as for any signs of maritime risk that can cause a transaction or parties involved to be flagged as suspicious.

This holistic approach to maritime AI will help banks fight financial crime and enable more business opportunities. Different lines of business, even those with no immediate links to shipping and trade, can enhance maritime due diligence without needing to develop further expertise.

Conclusion

Currently, 90% of globally traded goods are transported via maritime shipping, with maritime trade volumes expected to triple by 2050. As long as this growth continues, the financial industry will need to vastly expand the capacity of its risk assessment processes, moving beyond traditional screening to proactively identifying suspicious transactions and bad actors. This will be key to future-proofing the business, while minimizing valuable time spent on manual risk analysis.

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