UPI Pattern Red Flags: How Lenders and Forensic Auditors Detect Mule Accounts

India processed over 228 billion UPI transactions in 2025. That scale has also created cover. Fraudsters use mule accounts, bank accounts opened or hijacked specifically to route stolen funds, to move money through the UPI network faster than most institutions can trace manually.

According to the Finance Ministry’s data tabled in Parliament in November 2024, UPI fraud cases rose 85% in FY 2023-24 compared to FY 2022-23. By September of FY 2024-25, 6.32 lakh fraud cases had already been reported, amounting to ₹485 crore in losses, and these are only the cases that surfaced.

For forensic auditors and lenders, the real risk isn’t the fraud in isolation. It’s approving a loan or completing an investigation without spotting a mule account buried in the statements, because on the surface, these accounts look entirely ordinary.

What Exactly Is a UPI Mule Account?

A mule account is a legitimate-looking bank account used to receive and move illicit funds. It functions as a transit point in a money laundering chain. The account holder may be complicit or completely unaware, sometimes recruited under the guise of a “money transfer job” or similar pretext.

What makes UPI particularly useful to fraudsters is the combination of instant settlement and near-universal acceptance. Money moves in seconds, and once it does, recovery is difficult. The accounts themselves look ordinary from the outside, but their transaction patterns, when examined carefully, tell a different story.

5 UPI Pattern Red Flags That Signal Mule Activity

Spotting mule accounts requires looking beyond individual transactions. It is about understanding behavioural patterns across time, across accounts, and against the account holder’s stated financial profile.

1. Large Credits Followed by Rapid, Near-Total Withdrawals

The tell is consistent: money arrives in a lump sum and leaves within hours or days in smaller amounts spread across different counterparties. The account returns to a near-zero balance, sometimes within 24 hours. Forensic investigators refer to this as a FIFO (First-In, First-Out) pattern. Detecting it requires tracing the relationship between specific inflows and the outflows that follow, not relying on monthly summary totals.

2. Dormant Accounts Suddenly Showing High-Volume UPI Activity

An account with months or years of minimal activity that suddenly starts processing dozens of UPI transactions per week warrants immediate scrutiny. Fraudsters frequently acquire or rent dormant accounts precisely because their history appears clean. A dormant-to-active spike, especially one where the volume of UPI transfers far exceeds what the account’s stated occupation or income profile would justify, is one of the more reliable early indicators of mule use.

3. Structuring Across Multiple Transactions

Structuring means deliberately breaking amounts into smaller transfers to stay below reporting thresholds and avoid triggering alerts. In a UPI context, this looks like several rapid transfers of ₹1.9 lakh, ₹2.4 lakh, or similar amounts in quick succession rather than a single large transfer. Each transaction looks unremarkable in isolation. Without aggregating activity across the full statement period and matching counterparty patterns, structuring rarely gets caught.

4. Circular Transfers Between Related Counterparties

Structuring spreads activity across time. Circular transfers spread it across accounts. Mule networks frequently involve multiple accounts moving money between each other in a loop. A credit from Account A flows to Account B, which sends it to Account C, which routes a portion back to Account A. Each leg of the transfer appears legitimate on its own. Identifying circular transactions requires mapping counterparty relationships across multiple accounts simultaneously. Reviewing each account separately makes this pattern invisible.

5. No Correlation Between UPI Activity and Financial Profile

For lenders, this is the practical question: Does the UPI activity in this statement reflect the kind of business or employment the applicant claims? A proprietor showing ₹ 15 lakh in monthly UPI credits but filing quarterly GSTR returns of ₹ 3 lakh has a gap that demands explanation. The same applies to salaried individuals, where the volume and frequency of UPI transfers bear no relationship to salary credits or declared income. This cross-verification between bank data and GST returns is where many approval-stage risks surface.

Why These Patterns Are Difficult to Catch Manually

A trained analyst can identify one or two of these signals in a 12-month statement. But mule activity is designed to be distributed across multiple accounts and time periods, with counterparties that appear unrelated on the surface.

An analyst reviewing a single statement for a loan application will not catch circular transfers involving accounts that were never submitted. FIFO patterns go unnoticed unless someone is tracing how individual credits connect to subsequent debits, which rarely happens under time pressure. And when a DSA or NBFC is processing hundreds of applications per month, the depth of analysis that mule detection actually requires is not feasible, file by file.

This is the gap that automated analysis is built to close, and it’s precisely what Precisa’s AML dashboard is designed for.

How Precisa Surfaces These Patterns Automatically

Precisa analyses bank statements across multiple accounts simultaneously, flagging the specific signals that indicate mule activity.

The AML dashboard maps inter-bank transfers visually, showing exactly which accounts are sending and receiving funds and in what sequence. FIFO tracking identifies credit-to-withdrawal patterns at the transaction level, not in monthly aggregates. Counterparty detection consolidates activity by party across all uploaded accounts, making circular relationships visible without manual cross-referencing. The platform also flags dormant-to-active spikes and highlights UPI transaction volumes that are inconsistent with an account’s income or business profile.

Precisa processes UPI transactions with RRN (Retrieval Reference Number) detection, meaning individual UPI transfers are identifiable and traceable rather than grouped into undifferentiated totals. This is particularly relevant for forensic investigations where the source and destination of specific transfers matter.

Forensic auditors who once spent 30–45 days on a single investigation now complete the same cases in 25–30 minutes. For DSAs and lenders, the same application analysis that required two hours now takes 30 minutes, with more signals identified. Precisa supports 850+ banks and 1,200+ bank statement formats, serving 1,000+ clients across 25+ countries, with over 1.5 million bank statements processed to date. For lenders concerned about manipulated statements being submitted in the first place, Precisa’s Account Aggregator integration fetches bank data directly from the source, removing the opportunity for document-level tampering before analysis even begins.

For lenders who also want to cross-verify bank data against GST returns, the cross-analysis feature reconciles GSTR data and bank statement data over the same period, surfacing mismatches between declared turnover and actual credit patterns. This adds a layer of verification that single-source review cannot provide.

What Your Current Process Might Be Missing

Mule accounts do not announce themselves. They look like ordinary bank accounts until you trace what is actually moving through them. The patterns are present in every statement, but they require analysis at a scale and speed that manual processes cannot deliver consistently.

If your team reviews applications or conducts investigations where UPI volumes are significant, it is worth asking whether your current process would reliably catch a well-structured mule account. If the answer requires a pause, it may be time to see what Precisa flags on statements you have already cleared.

See how Precisa’s AML analysis flags mule activity automatically. Try Precisa for free and run it on statements your team has already cleared.