IIFL Finance: Lending on Autopilot with Sentinel
How IIFL Finance moved its risk team off hard-coded rules engines and onto a no-code policy studio — cutting policy deployment from four weeks to minutes.
IIFL Finance: Lending on Autopilot with Sentinel
How IIFL Finance moved its risk team off hard-coded rules engines and onto a no-code policy studio — cutting policy deployment from four weeks to minutes.
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the time IIFL takes to deploy a policy change after moving from a hard-coded rules engine to Sentinel's no-code studio.
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The challenge
Why IIFL needed to move off hard-coded rules
Multi-channel complexity
IIFL serves through proprietary apps, 40+ point-of-sale partners, and multiple DSAs — each channel with its own user characteristics. Risk needed to ship customised lending policies that aligned with each channel's profile.
Engineering as bottleneck
Every rule modification required intricate coding and extensive testing. Three to four weeks per change. Risk teams could not move at the speed the business required.
Limited risk visibility
Hard-coded engines could not surface timely, accurate risk reports. Aggregating risk was cumbersome, tracking historical rule changes was difficult, and the data team had to dredge logs to find growth opportunities.
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Outcomes
What changed after Sentinel went live
75% lower delinquency, 45% less fraud
Sharper rule logic and faster fraud-response cycles cut delinquency three-quarters and fraud incidence nearly in half. Approval rates 2x in parallel.
60% faster reviews, 30% fewer drop-offs
Application review time fell 60% with auto-decisioning at scale. Drop-offs dropped 30% as friction left the underwriting flow. Customer acquisition cost reduced 20%.
60% of IIFL loans now digitally originated
IIFL is now an industry beacon for level-four automation. With Sentinel's Policy Studio, IIFL is on the way to USD 1bn in originations.
In their own words
Voices from the rollout
Given the size and complexity of our digital lending operations, the transition from a hard-coded rules engine to a centralised system of rules management was inevitable. Sentinel isn't another run-of-the-mill BPM software, it's a comprehensive lending system in itself.
For lenders to have a fighting chance, the ability to make timely and well-informed decisions is critical. And this ability will remain elusive as long as code, spreadsheets, and data blind spots are involved.
Before and after
What Sentinel changed for IIFL
Three roles, three sets of pain points, three sets of capabilities — drawn from the deployment record.
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How it was built
What FinBox built for IIFL
IIFL Finance — a leading NBFC with proprietary apps, point-of-sale lending via 40+ partners, and multiple Direct Selling Agents — was running customised lending policies for a highly variegated user base on hard-coded rules engines. Risk teams could not ship a policy change without engineering. This case study walks through how Sentinel replaced that with a no-code policy studio, what changed for risk managers, CROs, and policy makers, and the impact on TAT, NPA, fraud, and CAC.
The hard-coded rules problemRisk teams dependent on engineering for every policy change. Three to four weeks per modification. Long lead times translating directly to lost sales and slow fraud response.
No-code policy studioAn intuitive graphical studio for modelling rules in natural language. Risk analysts and SMEs at IIFL define and model business logic as expressions and matrices — no code, no engineering tickets.
Familiar tooling for risk teamsExcel functions available as template tags, role-based access control, built-in test functions, and configurable alerts that act as an early-warning system on policy drift.
Simulation and canary deploymentTest new policies on historical data, then ship to a subset of live traffic in canary mode before full rollout. IIFL recovered lost-sale customers by re-running rejected applications through corrected policies.
On-demand reportingPerformance tracking at rule, policy, and partner levels. Funnel analytics that previously took 24 hours generate instantly. Visibility into why a user was flagged, approved, or rejected — without dredging logs.
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