Money Laundering via Global Financial System Estimated at Up to $2T Annually, AI Models May Address Issues, Banking Circle Explains

Banking Circle, a financial infrastructure developer, notes that McKinsey has found that around $800 billion to $2 trillion has been laundered annually via the international banking system.

Banking Circle reveals that criminals keep using more sophisticated methods to launder funds, which may put the reputations of financial services firms at considerable risk. Traditional anti-money laundering or AML models are just not fast enough, or “robust” enough, to deal with the “more complex” measures being used now by bad actors to avoid detection, Banking Circle confirms. The Fintech pointed out that “as the number of threats rise, costs are soaring, too.”

Banking Circle added that “to keep pace, financial institutions are adopting innovative technologies and developing policies that increase the likelihood of detecting illegal transactions.”

The Fintech company also noted that regulatory technology (RegTech) is now allowing companies or businesses to automate many different AML processes, which can significantly improve the manner in which checks are performed. They become “faster, more secure, and cost effective,” according to Banking Circle.

The company points out that “this is particularly prominent in transaction monitoring, where these new AML models commonly use artificial intelligence (AI) and data network analysis to significantly improve effectiveness by detecting the riskier, more complex flows that traditional systems have a harder time flagging.”

Banking Circle further noted that there are several benefits of adopting an appropriate AML model with the right or suitable regulatory technology at its core. By making the transition to artificial intelligence and data-based models, financial institutions are able to identify “more elaborate” types of fraudulent activities, Banking Circle explains.

The company adds:

“Data is enriched by using network analysis to connect companies, countries, and accounts to identify those with a higher risk of fraudulent transactions. These types of checks would be very difficult to perform manually, or at scale. Using network analytics, it is possible to examine the connections between entities, looking for similarities in known methods and typical behaviours that indicate a high risk of money laundering.”

Banking Circle also notes that AI-based models are able to consolidate several different risk factors simultaneously, along with their dependency on each other. Instead of considering just a few factors in a particular rule, an artificial intelligence model can effectively assess “a far higher number of dimensions on a transaction to extract a risk score,” the company explains.

Banking Circle points out that traditional service providers have been fairly slow when it comes to adopting the latest RegTech solutions into their existing AML processes, mainly because legacy systems and infrastructure make implementation quite challenging. The company adds that if relying or depending on a third-party to offer these solutions, incumbents are “typically more cautious and slower moving, with complicated procurement processes holding things up further.”

Banking Circle concludes:

“RegTech companies often provide solutions that focus on improving a niche area within AML, meaning that appointing multiple entities may be required to optimize the entire process. Ultimately, collaboration is the key to achieving better AML compliance, but until these challenges are addres:ed, the benefits that come with using RegTech to streamline AML will remain limited.”

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