Statement analysis for the files NBFCs actually receive.

NBFC credit grew 14.4% year on year to Rs 57.8 trillion by June 2026 per RBI data, and the growth is exactly where statement analysis is hardest: self employed borrowers, MSMEs, first time borrowers and geographies where the banking runs through co-operative and regional banks. The top fifty banks are the easy part; the friction lives in the long tail of formats, and in income that arrives as cash deposits and seasonal spikes rather than a salary credit on the first of the month.

A retail model stretched over these files misreads them: seasonal troughs read as distress, cash deposits read as unclassifiable, and the borrower who deserved the loan gets declined while the managed statement sails through.

• Business income, read properly. Trade inflows with self transfers netted and related party flows isolated, so declared income is real cash generation. Seasonality is fitted rather than flagged, and one offs are separated from the run rate.

• Obligations the bureau has not caught. EMI outflows cross checked against tradelines, surfacing app loans, BNPL repayments and fresh disbursals visible in the banking weeks before they report.

• Conduct across every account. EOD balance trends, zero balance days, utilisation, bounce counts with reason codes split technical versus non technical, month on month.

• GST alongside the banking. For MSME files, the companion GST view benchmarks GSTN declared turnover against banked credits, with variance computed and filing cadence read as conduct.

Thin file lending attracts the managed statement, and the July 2024 RBI Master Directions on Fraud Risk Management now extend structured expectations to NBFCs, with Early Warning Signals and Red Flagging frameworks mandated for the upper and middle layers. Every document through Fiscus gets a tampering verdict before analysis, transaction rules are tuned per segment, and every flag carries page level evidence for your analyst to adjudicate. The full stack is on the fraud detection page.

Minutes per full analysis, multi bank and 24 months deep, with CAM ready output that goes straight into the credit memo. Intake is native PDF, scanned PDF or Account Aggregator JSON, and the AA path matters here more than anywhere: NBFCs raised just over 60% of all AA consents in FY25 per Sahamati data. The dashboard onboards within 48 hours; LOS integration through unified REST APIs and webhooks takes up to two weeks. After disbursal the same data feeds early warning monitoring and collections prioritisation, so the statement keeps earning after sanction.

Four things, on your own files:

• Format coverage where it hurts. Run a co-operative bank scan and a regional bank statement, not the HDFC PDF every tool handles.

• Fraud depth with evidence. Include a file your team caught late. Check whether flags cite pages or just raise scores.

• Output your analysts keep. Compare the report against what your credit memo needs; count the rework minutes.

• Integration reality. Confirm API shape, webhook events and the actual timeline in writing.

Fiscus is built to win exactly this evaluation, which is why the comparison page publishes a 200,000 transaction benchmark rather than adjectives.

Per statement, scaling with volume, designed to come in lower than traditional analysis at scale. Cost tracks your origination curve: no platform licence to defend in a quarter where the book grows slower than planned.