Real-time detection is no longer a technical enhancement reserved for high-growth FinTechs; it has become a core requirement for consumer protection in a world of instant payments.
Consider a customer who is persuaded over the phone to urgently transfer their life savings to a so-called “safe” account, only to discover the funds have been wired directly to a fraudster. Or a victim of a romance scam who, over the course of three months, sends 11 separate payments totalling £7.5k before realising the relationship was fabricated.
According to Flagright, in both cases, the speed of modern payment rails meant the money disappeared in seconds. If institutions rely on end-of-day reviews or overnight alert files, the opportunity to intervene has already passed.
The rise of real-time payments, from instant bank transfers to crypto transactions, has fundamentally reshaped the fraud landscape. Authorised push payment (APP) fraud now represents a significant proportion of bank transfer losses in several regions, as victims are manipulated into willingly approving transfers. Because the customer initiates the transaction, recovery is often impossible once settlement occurs.
Meanwhile, money mule networks move stolen funds across multiple accounts and jurisdictions within minutes, deliberately exploiting any delay in monitoring systems. Romance scams, which often involve smaller but repeated payments, can evade static rules unless behavioural monitoring identifies emerging patterns. Impersonation and coercion scams are particularly dangerous, as criminals guide victims through security steps in real time, sometimes keeping them on the phone during the transfer.
These scenarios highlight a stark reality: when payments settle instantly, fraud must be detected instantly. Traditional batch-based AML systems, which generate alerts hours or even days after a transaction, are ill-suited to this environment. By the time a compliance analyst reviews yesterday’s alerts, funds may have been dispersed across mule accounts or withdrawn overseas. As highlighted in one Flagright case study, “a customer can open an account, transact funds, and close the account all in a single afternoon, much faster than legacy monitoring processes that sorted alerts at day’s end.” Batch detection does not prevent harm; it documents it after the fact.
Real-time monitoring shifts the model from reactive to proactive. Transactions are evaluated as they occur, using rules, behavioural analytics and machine learning models to assign risk scores within milliseconds. If a high-risk pattern is detected, such as an unusually large payment to a first-time payee or rapid outbound transfers from a newly opened account, the system can pause or block the transaction before settlement. In some cases, institutions may apply step-up authentication or contact the customer immediately to confirm intent. These brief intervention windows can mean the difference between preserving a customer’s savings and writing off a loss.
Behavioural signals are increasingly central to this approach. Real-time systems can assess device usage, session behaviour and historical transaction patterns. An unusually long online banking session combined with new payees and high-value transfers may signal coercion. Rapid inbound and outbound flows could indicate mule activity. Instead of relying on static thresholds, institutions can analyse context in the moment and intervene accordingly.
Flagright has positioned itself at the forefront of this shift. Its platform enables financial institutions to monitor transactions in real time and retrospectively within a unified environment.
Suspicious activity can be flagged as it happens, while compliance teams retain the ability to conduct historical analysis. One digital banking client reported an average API response time of approximately 0.44 seconds, allowing high-volume transaction screening without degrading customer experience. Dollarize, a cross-border FinTech, adopted real-time monitoring to “detect risks before they become threats,” underscoring the preventative focus of modern AML strategies.
Beyond technology, this evolution reflects a broader shift in the purpose of AML. Compliance is no longer simply about filing reports or satisfying regulatory audits. Regulators are increasingly scrutinising whether institutions could have prevented consumer harm, particularly in the context of APP fraud.
The UK’s reimbursement framework for certain APP scam victims, alongside the EU’s Instant Payments Regulation mandating measures such as Confirmation of Payee, signals mounting pressure to intervene in real time. AML and fraud prevention are converging around a shared objective: active consumer protection.
For FinTechs, banks and digital asset platforms, the message is clear. Real-time detection is not optional in an environment defined by instant settlement and sophisticated social engineering. It is a strategic imperative that protects customers, reduces reimbursement exposure and strengthens trust.
In an era where money can vanish in seconds, vigilance must operate at the same speed. Real-time AML ensures institutions are not merely recording fraud after it occurs, but actively stopping it mid-flight, reinforcing the fundamental promise of financial services: your money is safe.
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