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2026-07-28 · 7 min read

EMI Detection from Bank Statements: How It Works

How EMI detection APIs find loan repayments in bank statements: keyword matching, recurring-amount grouping, per-lender breakdowns, and debt-to-income math lenders actually use.

Read your own statement.

EMI detection starts with text signals: EMI, loan repayment, home/car/personal loan, and lender names (HDFC, Bajaj, Tata Capital, and others). But keywords alone over-count — one-off transfers mention 'loan' too.

The fix is recurrence: group keyword-matched debits by rounded amount and keep groups seen 2+ times as confirmed EMIs. Single explicit mentions are still reported, flagged unconfirmed, so analysts can review instead of missing them.

A useful response includes the count, total paid in period, estimated monthly EMI, and a per-lender breakdown with totals and counts — plus the raw samples so your team can audit.

Combine monthly EMI with income to get debt-to-income (monthly EMI / monthly income) and fixed-obligations ratios. Those two numbers drive more credit decisions than any other pair in the response.

Read your own statement.

Income, obligations and risk from one PDF. Free to start.

Or read the sample report