Real-time fraud and AML screening for cross-border remittances

Typical cut in false positives with ML (industry benchmark)

Decision incl. sanctions/PEP check, leading platforms

Fraud caught vs static rules, AI-assisted detection

Share of Nepal's GDP from remittances (World Bank)

The challenge

Remittances are the backbone of Nepal's economy, worth roughly a quarter of GDP, so the payment rails that carry them are a magnet for fraud and money laundering. The tools most operators use flag almost everything: rule-based transaction monitoring produces false-positive rates of 95 to 99% in typical deployments, which means analysts spend their days clearing alerts that turn out to be nothing while genuinely suspicious payments hide in the noise. That is slow, it is expensive, and it holds up honest customers' money. Nepal was placed on the FATF grey list in February 2025, and Nepal Rastra Bank's 2025 STR/SAR guidelines now expect fintechs, wallets and payment providers to run AI-assisted surveillance. The pressure to screen better, not just more, is real.

Our approach

Here is how we would build it. Every remittance streams in with its sender, recipient, amount and corridor. Instead of holding each payment in a manual queue, the system computes risk signals in real time: transaction velocity, device fingerprint, behavioural patterns and corridor risk. A machine-learning model trained on confirmed fraud and clean transaction history scores the payment, while a rules layer enforces hard policy checks and sanctions and PEP screening runs at the same moment, not as a separate slow step. The decision then branches. Low-risk payments clear and settle without a human touching them. Only high-risk ones are routed to an analyst, who confirms the case, files a suspicious-activity report to the FIU where needed, and feeds the outcome back so the model keeps learning. The compliance officer stays in charge of the judgement calls; the machine removes the drudgery around them.

Expected impact

Measured against published industry benchmarks, an approach like this is what good looks like. Machine-learning models trained on real outcomes typically cut AML false positives by around 60%, taking rule-based rates of 95 to 99% down toward 40 to 50%, so analysts spend their time on real risk. Leading real-time platforms return a screening decision, including sanctions and PEP checks, in under 300 milliseconds, fast enough to clear a payment before it settles. And AI-assisted detection has been shown to roughly double the fraud caught compared with static rules. For a Nepali remittance operator, that combination means honest payments move faster, fraud and laundering are caught earlier, and every decision leaves an audit trail that stands up to NRB and FATF scrutiny. These are cited industry figures showing what is achievable, not results from a specific NeuralYug deployment.

Most remittance fraud tools flag almost everything, then make a person clear the pile. This blueprint flips that: score every payment in real time, settle the clean ones instantly, and send only the risky ones to a human. Follow one payment through it below.

Why fewer alerts is the whole point

When 95 to 99 out of every 100 alerts are false, analysts drown and real fraud slips past. A model trained on confirmed outcomes learns the difference between an unusual-but-fine payment and a genuinely risky one, so the queue shrinks to the cases that actually need a human. That is where the speed, the cost saving and the better catch rate all come from at once.

Built with

Frequently asked

Are these numbers NeuralYug's delivered results?

No. This is a solution blueprint. Every figure is a cited industry benchmark, framed as what is typically achievable, not a result NeuralYug delivered for a client. Real performance depends on data quality, corridors and how the model is tuned.

Does automating screening mean fewer compliance checks?

The opposite. Every payment is screened for fraud and against sanctions and PEP lists in real time, so nothing skips a check. Automation removes the manual clearing of false alarms; the hard judgement calls still go to a compliance officer, who files suspicious-activity reports where needed.

How does this fit Nepal Rastra Bank's rules?

NRB's 2025 STR/SAR guidelines expect fintechs, wallets and payment providers to run AI-assisted surveillance and to report suspicious activity. A blueprint like this is built around that: it screens in real time, keeps a full audit trail, and routes confirmed cases to a human for STR/SAR filing to the FIU.