Payment fraud prevention has become one of the biggest challenges facing FinTech platforms in 2025.
High-profile cases, such as Cash App’s $255m settlement earlier this year, have highlighted the enormous risk weak anti-fraud controls pose to financial institutions, claims Resistant AI.
The company’s parent, Block, agreed to pay the sum after allegations that insufficient safeguards had allowed criminals to misuse its platform for money laundering and scams — a stark warning to others in the sector.
Fraud in payments is no longer a simple matter of stolen accounts or unauthorised transactions. It is a structural problem that starts the moment a user onboards. Criminals exploit weaknesses in KYC and KYB processes, often using falsified documents or synthetic identities to create fake accounts. Once they gain access, these bad actors move funds quickly through complex networks, exploiting systems designed for convenience and speed.
The fraud spreads rapidly across platforms, infecting transaction systems and requiring increasingly sophisticated defences. Modern payment providers are turning to layered security strategies combining onboarding verification, document authentication, transaction screening, and behavioural monitoring. These elements must operate in concert, not in silos, to prevent system-level breaches and catch suspicious activity early.
Payment fraud, at its core, is the misuse of payment platforms or applications to commit deception or financial crime. It targets two critical points in a provider’s system: onboarding and transactions. While traditionally considered separate, fraud and financial crime have increasingly merged. Techniques like authorised push payment (APP) fraud reveal how fraud schemes often rely on broader financial crime infrastructures such as money mule networks and shell companies. Conversely, much financial crime depends on fraudulent activities to succeed, blurring the line between the two.
Fraudsters exploit onboarding weaknesses to bypass verification and enter platforms undetected. They use synthetic identities — fake profiles built from real, fictitious, or AI-generated data — and create money mule accounts to move stolen funds. Without robust document verification and enhanced KYC/KYB procedures, these accounts can appear legitimate, granting criminals free rein. Institutions that rely only on basic checks for names and dates of birth expose themselves to enormous risk.
Once onboard, these fraudsters engage in transactional payment fraud, using their synthetic or stolen accounts to execute fast, high-volume transfers. The goal is to steal or launder money, often by exploiting legitimate platform features like instant transfers or peer-to-peer payments. AI-driven transaction monitoring has become a vital line of defence, capable of adapting to new fraud patterns more effectively than rigid, rules-based systems. Behavioural analytics powered by machine learning can flag anomalies across thousands of transactions, enabling fraud teams to focus on the most complex and high-risk cases.
Despite the power of AI, prevention remains the most effective strategy. Keeping fraudsters off the platform in the first place, through advanced onboarding verification and document integrity checks, reduces exposure to downstream risk. Definitions such as money mules—individuals or entities transferring illicit funds—and synthetic money mules—AI-generated identities used for laundering—are increasingly part of compliance teams’ everyday vocabulary.
As 2025 progresses, the payments sector continues to evolve in response to these threats. Success will depend not on single-point solutions but on integrated, layered defences that combine secure onboarding, intelligent monitoring, and coordinated fraud prevention frameworks.
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