Real-time payments are built for convenience, but recent incidents in Indonesia highlight a hard truth: the payment rails can be secure, while banks still get hit through the systems around them. When transfers settle instantly, attackers don’t need days. They only need seconds and one gap in application logic, access control, or monitoring.
That’s why the growth of real-time payments is also driving a new wave of fraud, one that targets the application layer and internal banking systems rather than the payment infrastructure itself.
The Growth of Real-Time Payments and the New Wave of Fraud
Real-time payments have quickly become the backbone of modern digital banking. Customers now expect transfers to move instantly, settle immediately, and work 24/7 without delays, cut-off times, or “pending” status.
But the same speed that improves customer experience is also reshaping the fraud landscape.
A recent high-impact incident showed how quickly things can spiral. Multiple banks reported abnormal transfers reaching hundreds of billions of rupiah. Early findings pointed to an important detail: the gap wasn’t in the national payment infrastructure itself, but inside the banks, specifically within internal applications connected to the payment rails.
In other words, even if the payment network is secure, the systems banks use to access it can still become the weakest link.
Why Real-Time Payment Fraud Is Harder to Detect?
Fraud has always been a race. Real-time payments simply remove the buffer that banks used to rely on.
In traditional transfers, institutions still had a window, however small, to review suspicious activity, trigger validation, or delay settlement. In instant payments, that window often disappears. By the time an alert is raised, the money may already be gone.
Regulators have also highlighted the growing sophistication of transactional fraud, including illegal transfers that are quickly routed through harder-to-trace channels such as crypto assets. Once funds are moved into these ecosystems, recovery becomes significantly more difficult, especially when detection happens after the fact.
This is why real-time payments demand a different approach. Fraud monitoring systems must operate at the same speed as the transaction itself, evaluating risk instantly and escalating anomalies without blocking legitimate customer activity.
Common Fraud Patterns in Instant Payments
Once payments move in real time, fraud rarely looks like a single “attack.” It shows up as a mix of tactics that exploit both human behavior and technical gaps, often at the same time.
Some of the most common patterns banks need to watch for include:
Unauthorized transfers at machine speed
Fraudsters often rely on stolen credentials, hijacked sessions, or compromised tokens. Once access is gained, transfers can be executed in seconds before traditional controls have time to react.
Exploitation of internal validation gaps
Recent incidents reinforce a hard truth; the payment rails may be secure, but attackers don’t need to break the rails. They only need to exploit weak points inside the bank’s application logic, transaction validation, or authorization layers.
Organized and cross-border schemes
Financial crime is increasingly coordinated. Funds can be fragmented, routed across multiple accounts, and moved across jurisdictions quickly, making the trail harder to follow once the transfer is completed.
Blind spots created by legacy monitoring
Many legacy detection systems still rely heavily on static rules. When fraud tactics evolve, those systems can fail to recognize new patterns, giving attackers a predictable window to operate.
Taken together, these risks show why real-time fraud is not just faster but also more adaptive, harder to trace, and more damaging when detection comes too late.
Why Rule-Based Fraud Detection No Longer Works for Modern Banking
For years, fraud detection depended on rules. Flag transactions above a certain amount. Block payments from high-risk regions. Trigger alerts when activity looks “unusual” based on fixed thresholds.
The problem is that modern fraud rarely fits those patterns.
Attackers have learned how to stay below limits, mimic legitimate behavior, and spread transactions across multiple accounts to avoid triggering classic red flags. Meanwhile, customer behavior itself is changing; instant payments, new digital channels, and 24/7 banking make “normal patterns” far less predictable than they used to be.
As a result, rule-based detection often struggles to keep up with new fraud types, produces too many false positives, and requires constant manual updates just to remain relevant
AI-driven systems, by contrast, learn from real data and adapt as threats change, making them far more suitable for modern financial ecosystems.
What Is Real-Time Transaction Monitoring for Banks?
To keep up with fraud that moves at the same speed as instant payments, banks need detection that works in real time, not after the transaction is already gone.
Real-time transaction monitoring is the continuous analysis of transactions as they happen, not hours later in batch reviews.
Instead of waiting for end-of-day reports, the system evaluates live payment activity instantly and looks for signals of fraud, such as unusual transaction patterns, abnormal behavior for a customer, or suspicious account relationships. When something looks off, alerts can be triggered immediately, allowing banks to intervene before funds are fully moved or losses escalate.
This shift matters because instant payments don’t leave room for delay. Once a fraudulent transaction is completed, recovery becomes significantly harder. Real-time monitoring turns fraud detection from “investigate after the damage” into detecting and responding while it’s still preventable.
How AI Improves Fraud Detection Accuracy
AI improves fraud detection because it doesn’t rely on rigid, static rules. Instead, it learns how legitimate transactions typically look, and flags behavior that deviates from that baseline.
In practice, AI can detect patterns that rule-based systems often miss, such as:
- subtle anomalies across multiple transactions
- unusual sequences of activity that don’t break thresholds
- hidden relationships between accounts, devices, and behaviors
Another major advantage is lower false positives. Traditional systems often overwhelm teams with alerts because they can’t tell the difference between “unusual but legitimate” and truly suspicious activity. AI models can make that distinction more accurately by analyzing context, history, and behavior patterns in real time.
For banks operating high-volume instant payments, this combination, speed, accuracy, and scalability, is what makes AI-driven monitoring far more effective than legacy approaches.
Introducing FRAML: Why Fraud and AML Must Work Together
In many banks, fraud prevention and AML still run in parallel. Fraud teams chase fast-moving threats like scams, account takeover, and unauthorized transfers. AML teams focus on longer patterns like laundering networks, layering, and compliance reporting.
But financial criminals don’t work in silos.
In real-time payments, a single incident can start as “fraud” and turn into “money laundering” within minutes. Stolen funds are quickly split, routed through mule accounts, and pushed out of the system before traditional controls can catch up.
This is why FRAML is becoming a key strategy in modern banking: Fraud + AML combined into one unified approach.
Instead of treating fraud and laundering separate cases, FRAML connects both sides of the story, so banks can see financial crime end-to-end. It helps institutions:
- Detect mule activity earlier
- Reduce blind spots between fraud and compliance teams
- Build a single risk view for investigations
- Stop suspicious transactions before the money disappears
In short, FRAML helps banks move from “flagging suspicious transactions” to actually disrupting financial crime in real time.
Adaptive Risk Scoring for Faster Decisions
Once fraud and AML signals are connected, the next challenge becomes speed. Because in instant payments, insight is useless if the decision comes too late.
Instant payments don’t give banks time to manually review transactions. Decisions must happen in seconds. That’s why banks don’t just need more alerts. They need adaptive risk scoring.
Adaptive risk scoring generates a real-time risk score using multiple signals, such as:
- Changes from a customer’s normal behavior
- Device or session mismatches
- Unusual location, network, or access patterns
- Transaction velocity and split-payment behavior
- Fund flows that resemble mule or scam patterns
- Risk signals across linked accounts
The key difference is that it’s dynamic, not static. It adjusts based on context, instead of relying on fixed rules. This allows banks to respond with the right level of action without hurting the customer experience:
- Low risk -> allow
- Medium risk -> step-up verification
- High risk -> block, hold, or escalate
For high-speed payment environments, adaptive scoring is one of the most practical ways to improve fraud response while keeping false positives under control. But even the best scoring model still depends on one foundation, knowing who is behind the transaction and whether the identity can be trusted.
How Sumsub Helps Banks Prevent Real-Time Payment Fraud at Scale
Even with strong monitoring, banks still face one major reality in instant payments: fraud prevention has to work at speed and at scale.
This is where Sumsub plays an important role.
Sumsub helps banks strengthen identity verification and fraud prevention across critical touchpoints, from onboarding to high-risk transaction flows. In real-time payment environments, this matters because fraud often begins before the transaction even happens, for example, when criminals take over accounts or use mule identities.
In practice, Sumsub supports banks by helping them:
- Reduce account takeover risk through stronger identity and behavioral checks
- Prevent mule account abuse, especially in high-volume transfer scenarios
- Support investigations with clearer identity signals and audit-ready evidence
- Accelerate decisions with workflows that adapt to different risk levels
The value is straightforward in instant payments; delay equals loss, and funds can move faster than traditional review processes can respond. That’s why banks need solutions that strengthen verification and fraud controls in real time, without creating unnecessary friction for legitimate customers.
Building Trust in Digital Banking with Smarter Financial Crime Prevention with Q2 Technologies
In real-time payments, fraud doesn’t wait, and banks don’t get a second chance. When transactions clear in seconds, prevention must move even faster.
Sumsub helps banks tighten fraud defenses where it matters most; identity, risk signals, and decision-making at scale. From stopping account takeovers to reducing mule account abuse, it gives fraud and compliance teams the visibility they need to act before funds disappear.
Through Q2 Technologies, part of CTI Group, banks can implement Sumsub in a structured way aligned with fraud monitoring workflows, compliance requirements, and operational realities. The result is faster detection, fewer blind spots, and stronger protection without adding unnecessary friction for legitimate customers.
Ready to strengthen your real-time payment security? Contact our team by clicking here.
Author: Wilsa Azmalia Putri
Content Writer CTI Group