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A loan applicant's account shows large credits, but the money belongs to other people's activity and leaves almost as fast as it arrives.
Compare total credits with closing balances, measure how quickly money leaves, and count unknown senders and same-day in-and-out movement.
Statements for the full period, counterparty names and UPI IDs from narrations, and the applicant's stated occupation or business.
A pass-through flag with the evidence, credits excluded from income, and referral under the lender's anti-money laundering process.
A mule or pass-through account receives money from many sources and moves it out within hours or days. The balance stays near zero while the credits look large. The money belongs to someone else’s activity, not the borrower’s business, so it cannot support a loan.
In India, banks and other regulated entities must follow RBI know-your-customer rules and report suspicious transactions to the Financial Intelligence Unit - India (FIU-IND) under the anti-money laundering law. A credit analyst’s job is not to investigate crime, but to spot the pattern and refer it under the lender’s process.
What does a pass-through pattern look like?
- Many small credits, then one large debit. Dozens of UPI credits from different senders, followed by a single transfer out.
- Near-zero closing balances. Money rarely stays overnight.
- Unknown senders. UPI IDs and names with no link to the stated job or business.
- Same-day in and out. Credits and debits of similar totals on the same day.
- A mismatch with the profile. A salaried applicant earning ₹30,000 a month moving several lakh a week.
Which genuine accounts look similar?
| Account type | Why it looks like pass-through | What to ask for |
|---|---|---|
| Collection agent | Collects from many customers, pays the principal | Agency agreement and remittance records |
| Trader | Buys and sells quickly on thin margins | Purchase and sales invoices, GST returns |
| Chit organiser | Collects subscriptions, pays the prize amount | Chit registration and member ledger |
| Society or RWA treasurer | Collects dues, pays vendors | Society records; usually a separate account |
A genuine business leaves a margin behind and has papers. A mule account usually has neither.
Worked example: one week of UPI credits
An applicant who says he is a salaried sales executive shows this in one week:
| Item | Amount |
|---|---|
| UPI credits received (63 entries) | ₹9,80,000 |
| Transfers out (same week) | ₹9,74,000 |
| Closing balance at week end | ₹6,000 |
The average credit is ₹9,80,000 ÷ 63 = about ₹15,556. Of the money received, ₹9,74,000 ÷ ₹9,80,000 = 99.4% left within the week. Only ₹6,000 stayed. For a salaried person, nearly ₹10 lakh in a week from 63 different senders does not fit the profile. The lender will exclude these credits from income and refer the file under its anti-money laundering process.
What should a new analyst do?
- Note the pattern with dates, totals and counts.
- Compare it with the applicant’s stated occupation.
- Ask a neutral question about the source of the money, if your process allows.
- Refer to your compliance or fraud team. Do not tell the applicant that a report may be made.
What to read next
Read suspicious counterparty patterns in bank statements next for the wider anti-money laundering signals. Counterparty spread analysis explains how unusual sender patterns are measured, and why lenders question large cash deposits covers the cash side.
A bank statement analyzer counts senders and money flow across a whole file in seconds. TransactIQ, a managed service on Terra Insight’s AWS Mumbai infrastructure, includes fraud and anomaly signals among its 40+ engineered credit signals, with an audit trail of every processed statement.