Real-Time Payments: The Dark Side of Instant Transactions

Real-time payment systems move instantly and irrevocably. So, we are now accustomed to the speed and the convenience. Instant transfer of funds puts financial institutions on hyperdrive. FIs have mere milliseconds to detect and block malicious transfers before they clear. Fraudsters are exploiting this narrow window of intervention.  ‍

Fraud in Real-Time Payments

One of the standouts in real-time payment fraud is Authorized Push Payment (APP). In APP fraud, victims are tricked into willingly sending money to fraudsters under false pretences. Because the victim authorizes the transfer, it bypasses many traditional fraud checks. 

Synthetic identity fraud has also shaken up the system, where fraudsters fabricate identities (mostly, a synthetic identity might combine a real stolen SSN with fictitious names and birthdates, or they may even be entirely AI-generated credentials) and abuse real-time payment networks.  

The rise of AI-charged fraud has further exacerbated it. Cases like Only Fake have shown that fraudsters don’t need to undergo the pain of physically manufacturing a fake ID. The reduced time and minimal skill mean that fraud can occur on a much larger scale.  

Not all RTP fraud is by external bad actors. Some is first-party fraud, committed by the legitimate customer. This fraud happens when a user makes a purchase or transfer and later falsely claims it was unauthorized or wants to reverse it. A person might send a real-time payment for goods, receive the goods, and then attempt to scam the bank into reversing the payment. 

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Why Traditional Fraud Tools Struggle in a Real-Time Environment

This fraud landscape is complex, where traditional systems may falter. They are designed for slower payment trails. When transactions must be settled within seconds, traditional systems cannot bear the strain. There is a “shorter window and limited resources for fraud teams to investigate and block transactions”, making thorough checks impractical. By the time a suspicious pattern is noticed, the funds have already moved.

Many banks still use rule-based engines that flag transactions based on predefined patterns. These systems struggle with evolving fraud tactics in real time. Tightening the rules often triggers false positives, and manual review is not feasible at scale. 

Limitations of KYC and AML in the RTP Era

A user can be onboarded legitimately (or a fraudster can successfully fake their identity), and only later engage in fraud or fall victim to a scam. Traditional KYC doesn’t continuously assess behavior or intent. RTP fraud patterns like first-party abuse or account takeovers often involve accounts that originally cleared KYC. 

This has led experts to stress continuous verification: monitoring users throughout their lifecycle, not just at onboarding. Moreover, AML/KYC systems are built to validate real identities against documents and databases. They struggle with synthetic identities, which often have no prior footprint to flag. 

AML Transaction Monitoring Delays

Anti-money laundering systems often operate on post-transaction monitoring – e.g., generating alerts on patterns that are reviewed hours or days later. In an RTP scenario, this is too late to stop the funds transfer. AML controls like suspicious transaction reports (STRs) help paint a picture after the fraud, but rarely prevent the initial crime. 

Gaps in Customer Due Diligence for RTP

Banks have historically assessed risk for credit products or large wire transfers with more scrutiny than for small retail payments. RTP blurs those lines – a user can open a basic account and suddenly send a large instant payment. If KYC vetting and account opening controls are not stringent, fraudsters or money mules can get accounts with ease.

In summary, KYC and AML are necessary but not sufficient in the real-time payment context. They provide a foundation (ensuring that at least a verifiable identity is attached to an account) but do little to stop fraud that uses legitimate customers or credentials. 

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Arresting Fraud Before It Occurs 

To combat real-time payment fraud, financial institutions are increasingly turning to proactive, pre-transaction detection technologies. Instead of relying on after-the-fact reports, these tools aim to identify and stop fraud before the payment is executed. 

Behavioral Biometrics & Analytics

Behavioral biometrics systems monitor how a user interacts with their device and app. These subtle signals create a unique user profile. If a session deviates from the legitimate user’s normal behavior, it may indicate the account is being controlled by a fraudster (or that the user is performing an unusual, possibly coerced action). For example, anomalies in typing rhythm or hesitation in navigation could flag that a normally confident user is being guided by a scammer during an APP fraud attempt. 

Device Fingerprinting and Intelligence

The device ID, OS version, browser configurations, IP address, geolocation, etc., collectively form a unique fingerprint. Banks can analyze if the request is coming from a known device or something suspicious. When a ‘fingerprint’ is involved in a fraud with a bank, the other bank can block transactions from the same device. Device fingerprinting is particularly useful for mule account scenarios, where one device might be getting used for running multiple accounts.  

AI/ML Risk Scoring Models

By ingesting vast amounts of customer and transaction data, AI/ML models can give risk scores to transactions in real-time. These models look at multiple data points: transaction velocity, amount deviations, time of day, recipient history, and cross-customer patterns that might indicate a scam or mule account. Not only are these models advanced, but they are also adaptable. If a new fraud pattern emerges, these models adapt to detect the patterns, unlike rule-based systems. 

Collaborative Data Networks & Insights

One bank cannot be the lone wager of war against real-time payment fraud because RTP fraud often spans multiple institutions (e.g., money mules hop stolen funds through several banks quickly). Here, collective intelligence is vital. Collaborative networks involve pooling fraud data across banks, fintechs, and even telecom companies to get a holistic view of emerging threats. 

For example, India’s central bank established a Central Payments Fraud Information Registry (CPFIR) in 2020, where all regulated entities must report payment fraud incidents. In the U.S., Early Warning Services (owned by a consortium of banks) not only runs Zelle but also acts as a data-sharing hub to identify suspicious accounts and login devices across member banks. 

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The Regulatory Landscape and Liability in Real-Time Payments

The rise of RTP fraud has increased the pressure on financial institutions to take responsibility. In the UK, which has been hit severely by APP scams, both the sender and receiver bank must compensate the victim (up to £85,000). There’s no blanket reimbursement rule in the EU. It is focused on preventing RTP fraud, not compensating the victims. The current PSD2 regulation gives strong protection for unauthorized transactions to consumers. The region does little for the victims of APP scams, where there is a voluntary action from the consumer, so it’s not legally ‘unauthorized.’ 

In the US, real-time payments via systems like Zelle are governed by laws that distinguish between unauthorized fraud (covered by Regulation E of the Electronic Fund Transfer Act) and authorized scams (not covered). If a fraudster hacks your account and sends money, Reg E mandates the bank to make you whole. But if you were tricked into sending money yourself, the law views it as an authorized transaction, leaving the victim responsible. 

India has no legal requirement for banks to reimburse UPI scam victims. There’s growing debate in India about this, especially as losses mount to levels that could undermine trust in digital payments.  Worldwide, the approach differs because a “one-size-fits-all regulation is unrealistic,” given different cultural and political environments. Instead, each region is experimenting -- some via tech mandates, others via liability shifts or collaborative frameworks. Ultimately, both regulation and innovation will likely be needed. 

More countries will refine their stances on liability. Regulators are also likely to demand faster sharing of fraud data across institutions (for instance, mandating reporting of mule accounts into central databases)

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Conclusion

While real-time payments come with a “darker side” of fraud risk, the industry is not standing still. The arms race between fraudsters and defenders is driving unprecedented innovation in financial security.  

The narrow opportunity to arrest fraud was inevitably going to pave the way for exploitation. The convenience and how integral RTP has become will mean that innovation efforts are not going to stop. Moreover, regulations will continue to evolve, keeping in line with the fraud tactics and AI’s advancement. 

Technology, collaboration, and regulation collectively ensure that real-time payments remain safe and trusted even as criminals try to exploit every millisecond. 

Arya.ai has been assisting banks and financial institutions in arresting fraud with preventive mechanisms. If you’d like to discuss how we can help, please connect with us.