FIs Need Collaboration AND Technology to Win AML War

Popular images of international crime fighting are usually replete with bank heists, gun fights, drug smuggling, and undercover agents in dark suits and shades.

But behind every global crime network supporting crimes, from terrorism to fraud, lies the vast and obscure business of money laundering — what Gudmundur Kristjansson, founder and CEO of Icelandic anti-money laundering (AML) software startup Lucinity, refers to as “the crime that fuels crime.”

See also: Anti-Fraud and Anti-Money Laundering Are Two Sides of the Same Coin

In recent years, banks, businesses, governments and law enforcement agencies around the world have injected billions of dollars into combating these financial crimes and preventing the suspicious transactions that enable criminals to clean their “dirty” money.

But as Kristjansson said, that hasn’t stopped the bad actors from improving the efficiency with which they launder illicit funds. While the amount of money spent on AML has increased over the years, the percentage of all money laundering that was actually prevented has decreased.

To solve this problem, Kristjansson said it is critical to redesign the toolkit that financial institutions (FIs) use to fight money laundering. And it’s what Lucinity has done, developing artificial intelligence (AI)-powered, user-centric compliance systems that simplify complicated data and help banks and FinTechs boost compliance productivity with less time and financial resources.

The Reykjavik-based firm, which counts Pleo and Visa-owned Currencycloud among its customers, raised $17 million in a Series B round earlier this month and launched a partnership with United States credit reporting company Experian to provide ongoing know your business (KYB) and risk assessments.

Read more: AML Software Firm Lucinity Raises $17M

The AI Approach in AML

In the world of AML tech, machine learning (ML) algorithms are routinely applied to monitor large volumes of transactions and identify suspicious activity. But the problem with the AI approach is that algorithmic outputs are rarely ever black and white.

Although AI can do much of the work by scanning millions of transactions, making sense of the patterns AI observes is one of the main challenges AML professionals face today, Kristjansson explained, adding that prior to deploying Lucinity’s solution, many of the company’s clients reported spending 80% of the time devoted to AML simply translating system outputs into meaningful information.

See also: 5 EU FinTechs Using AI to Support Consumers, Businesses

Following the insight that “you don’t need to use AI just to detect money laundering, you can actually use AI to explain patterns to a human in a way that we have struggled to see,” Lucinity has built a suite of AML tech that emphasizes human-readable outputs and the application of AI not just to filtering, but also to presenting data, he said.

Yet even though the company is good at detecting these patterns and explaining them, Kristjansson acknowledged that highlighting potential money laundering is still only half the battle.

“When it comes to reporting of financial crime, there is a lot to do, of course, on the regulatory side,” he said.

Data Sharing Is Key

While European data protection laws prevent banks and businesses from sharing customer data without their consent, ideally, banks should be able to share data among themselves and with law enforcement agencies, Kristjansson noted. In practice, however, “the legal frameworks, […] especially in the EU, do not allow that at the moment,” he explained.

Read also: 5 EU Startups Making Waves in the AML Technology Space

It is the reason why FIs such as the European Banking Authority (EBA) have called for a better data-sharing framework to combat financial crimes in the European Union, a strategy that could be included in the upcoming review of the revised Payment Services Directive (PSD2) regulation, Kristjansson said.

See more: EU Banking Authority Payment Fraud Consult May Impact PSD2

“In the new directive, we are hoping that it will be much easier to share data and share suspicions,” he said.

Yet despite the currently limited framework for sharing transaction data and suspicious actor information, Kristjansson said he is optimistic about the possibility of greater interinstitutional collaboration on AML in Europe.

“What we do is we share the knowledge graph that we’re building [to outline the] behavior we’re seeing in [a particular] entity,” he said, adding that the more banks and FIs join the Lucinity network, the more accurate that “knowledge graph” becomes.

In that sense, Lucinity’s current approach to interbank sharing focuses “not on sharing the data,” he said, “but sharing the knowledge of how to find the path.”

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