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Banks are increasingly turning to AI- and machine learning (ML)-based solutions to prevent fraud more cost-effectively and efficiently.
Factors like the growing adoption of real-time payments and rise of cross-border payments are making banks more vulnerable to fraud, which has driven tech providers to better tailor solutions to combat these fraud types and mitigate losses: Losses from e-commerce, money transfer services, and banking services are expected to jump to $28 billion annually by 2023, up from an an estimated $22 billion for 2018, per Juniper Research.
Two new solutions were introduced earlier this week, showcasing the use of AI in fraud prevention:
- Financial software giant Finastra launched a fraud-detection tool for financial messaging in partnership with Swiss fintech NetGuardians at the Sibos 2019 conference. The solution is tailored to the SWIFT Network, which global banks and large companies use to send transaction-related messages. The tool leverages AI and ML to spot fraudulent payments in real time and stop them before money leaves the bank. It uses an algorithm to learn a bank’s payment patterns, which enables it to identify high-risk message irregularities, allowing it to both prevent fraud and avoid false positives, which could lead to unnecessary fraud investigation.
- FICO introduced Falcon X, an AI- and ML-powered solution to prevent fraud and financial crime, at the Finovate conference in New York. The solution streamlines fraud detection and anti- money laundering (AML) processes. The rapid adoption of real-time payments — like mobile payments or peer-to-peer (P2P) transfers — has enabled greater money laundering, as money can be moved quickly between accounts. Meanwhile, it’s also fueled other financial crime, like drug trafficking, per Jason Keegan, who oversees the fraud line of business for FICO. These fraud types largely depend on the same solutions: FICO estimates an 80% overlap in software functionality between legacy fraud and AML—making it more cost effective to adopt a solution that addresses both, rather than two independent solutions.
AI and ML are integral in identifying fraud in real time and curtailing false positives through behavioral analysis. Investing in fraud prevention measures can be extremely costly for banks: US FIs spent $25.3 billion to manage money laundering risks in 2018 — primarily because AML efforts are largely manual, making them inefficient and hard to scale.
But the implementation of AI and machine learning has the potential to cut fraud prevention costs: 52% of banking professionals surveyed by Finextra Research expect that AI will transform AML, compliance, and fraud prevention operations within the next five years — making them the use cases with the most transformation potential, according to banks. And AI can help banks bolster fraud detection and prevention by discerning patterns that humans would be unable to detect.
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