With instant payments offering no time for reversals, AI has become the industry’s most important tool for detecting fraud before money moves.
Calls for faster payments from consumers and businesses are only getting louder, with real-time payment systems becoming an increasingly important part of the global payments infrastructure.
Whether it’s on the UK’s Faster Payments system, SEPA Instant in Europe, or RTP networks in the US, the ability to move money within seconds is now a modern expectation.
However, the speed which makes these systems attractive also introduces several challenges because shortening the settlement time means shrinking the window available to detect and prevent fraudulent activity.
Unlike traditional card payments, where transactions can be reversed or disputed after settlement, real-time payments are usually irrevocable. Once funds are sent, they are difficult to recover, placing a greater emphasis on preventing fraud before the transaction is completed.
This has left banks, payment service providers and fintechs to go back to the drawing room on how fraud detection works, which has led to the adoption of AI.
Where AI fits in the payment flow
In real-time payments, fraud checks are embedded into the authorisation process and must happen almost instantly without introducing friction that would undermine the speed of the transaction.
When a user initiates a payment, data points are captured in milliseconds, including transaction value, recipient details, device information, location and behavioural signals such as typing patterns or transaction history.
AI-driven fraud systems analyse this data in real time to generate a risk score, which is then used to determine whether to approve the payment, decline it or trigger additional verification steps.
A change from rules‑based systems
Older fraud‑detection tools are mostly built around fixed rules, which might set limits on transaction size, highlight unusual locations, or block payments to merchants known to be high‑risk.
These systems can still catch certain types of fraud, but have clear limits. For instance, they don’t adapt easily, they need people to update them manually and they often miss new fraud techniques.
AI models look for patterns across very large datasets and spot behaviour that doesn’t fit. This includes learning what ‘normal’ activity looks like for each user and flagging anything that falls outside that pattern.
For example, a payment sent to a new recipient, at an unusual time, for an amount that doesn’t match past behaviour might be treated as higher risk even if none of those factors on their own would break a traditional rule.
AI systems can also evolve over time. By feeding the system more and new data, AI can highlight new fraud trends and adjust automatically without needing constant manual updates.
Use cases in real‑time fraud detection
AI is especially useful for tackling fraud types that have become more prominent alongside real‑time payments.
Authorised push payment (APP) scams are an example, with levels rising over recent years in the UK.
In these cases, criminals trick users into voluntarily sending money usually by posing as someone they know or other social engineering tactics. Because the user is the one initiating the payment, detecting fraud means analysing context and behaviour and not just checking identity.
AI models can pick up signals linked to these scams, such as sudden changes in how someone normally pays, first‑time transfers to risky accounts or patterns that match known scam techniques.
Account takeover is another area, with AI able to track login behaviour, device usage, and transaction patterns to spot when an account is being used in a way that doesn’t match its usual profile.
Mule account detection also relies heavily on AI, as by examining how money moves across many accounts, it can identify those being used to receive and pass on illegal funds.
Trade‑offs and challenges
While there are many strengths to using AI, using the technology for fraud detection comes with its own challenges.
One issue is balancing fraud prevention with customer experience. If models are too strict, they can create false positives, blocking or slowing down legitimate payments which can be very frustrating when payment is expected to be instant.
Explainability is another challenge as some AI models, especially more complex ones, are hard to interpret. Banks still need to understand why a transaction was flagged or declined, both for internal oversight and to meet regulatory requirements.
There are also technical limits because fraud decisions must be made in milliseconds, which restricts how complex the models can be and means highly efficient infrastructure is needed.
However, perhaps the biggest challenge is data, a problem which appears across almost every area where AI is used.
AI systems rely on strong, consistent, high-quality data, and when the data is incomplete, inaccurate, or unreliable, model performance drops. This leads to weaker fraud detection, higher overall risk and a rise in false positives which can disrupt legitimate customer activity.
