Walk into any MSME credit appraisal meeting at an NBFC and you’ll find the same three artefacts: audited financials, bank statements, and a CIBIL report.
What you won’t find: a GST filing trend analysis.
This is a significant blind spot, and it’s costing NBFCs both portfolio quality and market share in the MSME segment.
WHAT GST DATA ACTUALLY TELLS YOU
GST data, available through Account Aggregator and direct API integrations, provides quarterly revenue, sectoral purchase patterns, customer concentration, input credit trends, and filing regularity. For an MSME that doesn’t produce monthly MIS reports, it’s the closest proxy to real-time business performance that exists.
Filing regularity alone is informative: a business that switches from monthly to quarterly filing is often doing so under cash flow pressure, not because their business grew slower. A business with consistent quarterly filing over 4+ years has demonstrated operational stability that a single bank statement cannot.
WHAT THE DATA SHOWS
We ran a comparison across 3,000+ MSME loans in our portfolio analytics. GST trend data, specifically the direction of quarterly revenue change and filing regularity over the past 8 quarters, has a higher predictive correlation with 90-day DPD than bank balance alone.
Not higher than bureau score. Higher than bank balance, which is the secondary data point most NBFC credit models rely on after CIBIL.
For MSME loans below ₹50 lakh, the predictive gap is even wider. Bank balances in this segment are highly volatile and easily managed for a short window pre-application. GST filing history is much harder to engineer.
THE LIMITATIONS
GST data has a 1-3 month lag between the filing period and API availability. This is a real limitation for at-origination underwriting in a fast-moving business. We address it by combining GST historical trend (3 years, quarterly, via Account Aggregator) with bank statement analysis for current-month cashflow signal. The combination is more predictive than either source alone.
WHY NBFCs ARE NOT USING IT
Three reasons. First: legacy LOS that doesn’t pull GST via Account Aggregator API. Second: underwriters not trained on interpreting GST data in a credit context. Most credit teams know how to read a P&L, not a GST filing pattern. Third: credit policy that still mandates audited financials as the primary evidence and hasn’t been updated to accommodate alternative data.
All three are solvable. UltraBanker’s LWS pulls GST data via Account Aggregator at origination and tracks filing patterns through the loan lifecycle as a monitoring signal. The underwriting model weights it alongside bureau data. The CAM AI Agent translates the GST trend into plain-language credit commentary that an underwriter can include in the credit memo without being a GST expert.
The MSME borrower without three years of clean audited financials isn’t necessarily a bad credit. They’re someone whose business story isn’t being told in the language your LOS understands yet.
See how LWS handles MSME underwriting with alternative data at AI Loan Scoring & Underwriting Software | UltraBanker.