How QuickFund Financial Services Automated Loan Underwriting and Cut Approval Time from Days to Minutes

QuickFund Financial Services had grown its loan book past ₹200 crore largely on the strength of its underwriting team's judgment — but that same manual process had become the ceiling on how much further the business could grow. Loan applications were reviewed by hand, and even straightforward cases took five to seven business days to get a decision, mostly because underwriters were manually cross-referencing bureau reports, income documents, and bank statements one application at a time.

KYC verification relied on physically collected documents, which meant delays before an application could even enter the review queue. Worse, credit risk assessment varied meaningfully between underwriters — there was no standardized, data-driven scoring model, just individual judgment shaped by each underwriter's own experience. That inconsistency was a real risk, not just an efficiency problem.

Leadership had no real-time view into the health of the loan book. NPAs, disbursement trends, and collections were compiled by hand into monthly reports, meaning problems could go unnoticed for weeks. And every quarter, the finance team spent days manually assembling RBI-mandated regulatory reports from a patchwork of spreadsheets — time that could have gone toward actually managing risk instead of documenting it after the fact.

Because this was a regulated lending business, we started with QuickFund's risk and compliance teams, not the engineering backlog — working through RBI's digital lending guidelines together before designing a single workflow. That sequencing mattered: the credit scoring model we eventually built came directly from translating QuickFund's own underwriters' existing manual assessment logic into a structured, data-driven scoring system, rather than importing a generic off-the-shelf model.

The platform integrates Aadhaar and PAN verification APIs so identity checks that used to take days now complete in minutes, alongside a hybrid rules-based and machine-learning credit scoring engine, e-signature for loan agreements, a real-time portfolio dashboard, and automated RBI regulatory reporting generated directly from live loan book data.

We didn't let the automated scoring engine make a single live decision until it had earned that trust: for six weeks, it ran in parallel with manual underwriting, and we compared every automated recommendation against what an experienced underwriter actually decided. Only once that comparison showed consistent accuracy did QuickFund's compliance team sign off on a phased go-live — starting with smaller loan tickets and expanding to larger ones only after the system built a track record.

The result respects a principle QuickFund was firm about from day one: automation should handle the clear-cut cases, and route anything ambiguous to a human underwriter rather than force a decision either way.