Fighting FinCrime: How are FSIs using technology to crack down on money laundering?

The panel opened with the panellists giving their views on the challenges facing FSIs in carrying out a successful digital AML strategy. “There’s a rising prevalence of the ‘creativeness’ of financial criminal activity out there,” said Zowie Lees-Howell, VP of enterprise sales at SmartSearch, noting that this can thwart some of the approaches currently being used to mitigate fraudulent activity.

The next challenges Lees-Howell highlighted were the volume of data organisations are consuming to help in terms of finding the right decision, increasing costs and regulatory requirements around compliance, and increased customer demand for digital banking activities. “A challenge for most FSIs lies in determining what the risks and advantages are of utilising tech such as AI to address AML,” said Aaron Elliott-Goss, head of financial crime and fraud at Revolut. “When talking about deploying models to look at fraud and money laundering, there is a consideration around whether such models have gone through the right amount of governance, validation, and have the right data modelling in place.”

Elliott-Goss also noted that implicit bias which such models may be generating is a further concern. “It’s clear to me what benefits are there but it’s now about how to manage the risks within a mature and controlled environment,” he concluded. “One thing about AI and machine learning is that it tends to only be as good as the data you put into it,” said Ronya Naim, head of financial crime compliance at ClearBank. “Getting and gathering that data is often the biggest challenge for us as finance institutions.” Andrew Robinson, compliance officer, Sompo International Global Markets highlighted the challenge of transactional analysis and getting it in a state wherein it can be meaningfully analysed by tech like AI and machine learning. “The stage we need to get to is to be able to take all of this data we have and be able to analyse the combination of the information and determine how best to make use of the conclusions reached in fighting AML,” he said.

Elliot-Goss agreed there is an ongoing need for AI and machine learning tech to continue being able to ingest high quality data. “It’s worth exploring, beyond internal data, what other external data sources are available that could be leveraged to gain more information on the individuals being onboarded on to our respective platforms,” he said. He pointed out matters like knowing the history of a user’s email address, registered phone numbers and social media activity are other factors which could make more informed choices about accepting customers at the outset.

The panel wrapped up with the speakers concluding that technology has an important role to play in detecting fraudulent activity – there just needs to be more work done in providing useful data to ensure technology can become as useful a tool as possible in the fight against fraud.

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