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Fabricated bank statements are often constructed with deliberate variety in counterparty names to appear realistic. However, the resulting distribution — many counterparties at similar frequencies — is structurally unlike genuine accounts, which show strong concentration in a few dominant payees and a long tail of occasional transactions.
Compute the counterparty frequency distribution across all transactions. Measure concentration: what share of transactions involve the top 5 counterparties? What is the ratio of unique counterparties to total transactions? Genuine accounts typically show the top 5 counterparties accounting for 30–60% of transactions; fabricated accounts show a flatter distribution with no dominant names. Also check for the absence of expected Indian consumer/business counterparties given the account type.
Calibrate for account type (salary vs business current vs MSME). Exclude self-transfers and ATM entries from counterparty analysis. Apply minimum transaction count threshold (50+ transactions for meaningful distribution analysis). Cross-reference against known Indian consumer platform names for consumer account validation.
Counterparty concentration metrics (top-5 share, unique counterparty ratio), distribution classification (concentrated / normal / unusually flat), and a flag for absence of expected counterparties given account type, presented in the fraud signals section of the analysis report.
Every active bank account develops a characteristic pattern: a small group of counterparties that dominate transactions, surrounded by a longer tail of occasional payees. Salary arrives from one source; rent leaves to one landlord; groceries go to a handful of regular platforms; the occasional one-off transaction adds variety. Fabricated statements lack this structure — the person inventing transactions tries to create variety and ends up producing a distribution that no real account generates.
Counterparty spread analysis makes this structural difference measurable.
What Counterparty Spread Analysis Checks
The check examines two related properties of the counterparty distribution in a bank statement:
Concentration: What fraction of total transactions involves the top 5 or 10 counterparties? In genuine accounts with six or more months of history, a small number of repeat counterparties — the salary payer, the utility company, the primary food delivery platform, the loan servicer — typically account for 30 to 60% of all transactions. The rest are spread across a longer tail of occasional payees.
Spread uniformity: How evenly are transaction counts distributed across the full counterparty list? Genuine accounts show a steep concentration curve — dominant names at the top, many names appearing only once or twice. Fabricated accounts constructed with deliberate variety tend to show a flatter curve: many names appearing 3 to 7 times each, with no clear dominant counterparty.
These two properties are related but independently measurable, and a fabricated statement can trip one without the other depending on how it was constructed.
How It Differs from AML Round-Trip Detection
Counterparty spread analysis is a fabrication detection tool. It asks whether the distribution of payees looks like genuine organic spending, irrespective of whether the account is real or the funds flow is suspicious.
AML round-trip detection is a fund-flow analysis tool. It identifies credit-debit pairs with the same counterparty at similar amounts and short time gaps — the pattern of money leaving an account and returning through the same or related party, which can indicate circular transactions that inflate apparent turnover.
Both checks involve counterparties, but they address different fraud types. A genuine account with suspicious fund-flow behaviour may fail round-trip detection but pass spread analysis. A fabricated statement with artificial variety may fail spread analysis but have no round-trip pairs — because there were no real funds moving.
Counterparty Distribution Reference Table
| Distribution Pattern | Signal Interpretation | Recommended Action |
|---|---|---|
| Top 5 counterparties above 50% of transactions | Strong concentration — typical of genuine primary accounts | No spread-based fraud signal |
| Top 5 counterparties at 30–50% | Normal — moderate concentration with healthy variety | No spread-based fraud signal |
| Top 5 counterparties below 20% | Low concentration — unusual for an active primary account | Review counterparty list; check for expected names |
| 80%+ counterparties appearing only once | Heavy one-off pattern — could indicate transaction inflation | Investigate volume; check for duplicate names with slight variations |
| No recognisable Indian consumer platform present (salary account) | Absence of expected counterparties | Cross-check against declared account purpose; request clarification |
| High transaction count but uniform counterparty frequency | Flat distribution — strong fabrication signal | Treat as high-priority flag alongside other forensic signals |
India-Specific Context
Indian consumer accounts present a useful set of expected counterparties. A genuine salaried individual’s primary account active over 12 months would typically show names from a recognisable set: PhonePe or Paytm for UPI transactions, Swiggy or Zomato for food delivery, Amazon or Flipkart for e-commerce, an electricity board or telecom provider for utilities, a bank or NBFC for EMI debits. The presence of these widely used platforms is not itself a fraud check — their absence in a consumer account with even counterparty spread is a notable gap.
For NBFC underwriting, the account’s stated purpose matters: an MSME current account would not be expected to show Swiggy transactions, but it should show concentrated vendor names and client names consistent with its declared business activity. An MSME statement that shows 200 counterparties at roughly equal frequency — with no dominant vendor or client — does not match the account behaviour of a functioning business.
The RBI Master Direction on KYC requires regulated entities to verify customer documents for authenticity and consistency. Counterparty spread analysis is one structured way to assess whether a submitted bank statement is internally consistent with the account holder’s declared financial activity.
The bank statement fraud detection capability runs counterparty spread analysis on every uploaded statement and presents the concentration metrics and distribution classification alongside other forensic signals.
The bank statement analysis platform automatically extracts and classifies counterparty names across 34+ Indian bank statement formats, enabling the spread check to operate on clean, parsed counterparty data rather than raw narration strings.