> ## Content Index > Fetch the complete content index at: https://research.finbox.in/llms.txt > Use this file to discover other available public pages before exploring further. # Sentinel AI transforms risk and decisioning at IIFL - A FinBox case study - URL: https://research.finbox.in/case-studies/sentinel-ai-transforms-risk-and-decisioning-at-iifl-a-finbox-case-study/ - Published: 2026-05-04T18:22:37.000Z - Updated: 2026-07-10T12:42:13.000Z - Description: Sentinel AI turns IIFL finance into a digital lending powerhouse. - Author: Mayank Jain - Tags: Case-study, #Sentinel, Sentinel, #case study, #gated, gated [ ](https://finbox.in/?ref=research.finbox.in) [Products](https://finbox.in/products?ref=research.finbox.in) [Solutions](https://finbox.in/solutions?ref=research.finbox.in) [Resources](https://finbox.in/resources?ref=research.finbox.in) [Company](https://finbox.in/company?ref=research.finbox.in) [Request Demo](https://finbox.in/demo?ref=research.finbox.in) Case Study # 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. Chief Risk Officers Risk Policy Teams Heads of Digital Lending at NBFCs Lending Engineering Leaders IIFL Finance: Lending on Autopilot with Sentinel Case Study ### 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. Download free No paywall. Email required to receive the file. 4 weeks → minutes the time IIFL takes to deploy a policy change after moving from a hard-coded rules engine to Sentinel's no-code studio. Built for credit teams. No paywall, no sales follow-up unless you ask. Download case study The challenge ## Why IIFL needed to move off hard-coded rules 01 ### 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. 02 ### 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. 03 ### 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. Get exclusive access — free, no paywall. Download free Outcomes ## What changed after Sentinel went live Insight 01 ### 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. Insight 02 ### 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%. Insight 03 ### 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 Why Sentinel won the bake-off 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. Mehekka Oberoi Director, IIFL Open Neobank Strategy, Chairman's Office (IIFL Group) Why this matters for lenders 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. Rajat Deshpande CEO and Co-Founder, FinBox Before and after ## What Sentinel changed for IIFL Three roles, three sets of pain points, three sets of capabilities — drawn from the deployment record. Role Before Sentinel With Sentinel Policy makers — 3-4 week dev cycles for rule changes — Rule-change comms were error-prone — Manual simulation comparisons — Policy comparison long-winded — Zero dev dependency — Policy changes in minutes — Self-serve UI simulator — 3-click simulation reports Risk Managers — Hard to spot growth opportunities — Tracing bad-outcome rules was taxing — Rule-change history hard to track — Aggregating risk did not scale — Policies ranked by performance — Visibility on every decision — Dashboards on demand — One-click rule rollback CROs — Long dev cycles meant lost sales — Slow fraud response — Manual tasks dominated team capacity — React to shifts fast — Configurable EWS alerts — Lean team, sharper risk Download the case study — the full version, free. Get the case study 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. 01 **The hard-coded rules problem**Risk 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. 02 **No-code policy studio**An 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. 03 **Familiar tooling for risk teams**Excel 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. 04 **Simulation and canary deployment**Test 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. 05 **On-demand reporting**Performance 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. ## Download the case study — free Email it to yourself in under 30 seconds. No paywall, no sales follow-up unless you ask. Get the case study Related resources ## Other reading from FinBox [ReportState of Digital Lending 2026A comprehensive guide to trends, insights, and best practices across India's eight digital credit categories — with FY19–FY25 actuals, FY26–FY30 outlook, and senior practitioner perspectives.Read →](https://research.finbox.in/state-of-digital-lending-2026/) [WhitepaperReimagining Housing Finance with Sentinel AISmarter co-applicant underwriting and guarantor verification — identity, compliance, credit, and income signals in a single decisioning engine for HFCs.Read →](https://research.finbox.in/re-imagining-housing-finance-with-sentinel/) [GuideThe Business Rules Engine: A Lending ImperativeWhy hard-coded policy is the silent tax on digital lending — and how to fix it without a rebuild.Read →](https://research.finbox.in/business-rules-engine-lending-imperative/) © FinBox 2026\. All rights reserved. [Privacy](https://finbox.in/privacy?ref=research.finbox.in)· [finbox.in](https://finbox.in/?ref=research.finbox.in) Download the case study ### Download the case study Get "IIFL Finance: Lending on Autopilot with Sentinel" emailed to you. × ## Related in the Business Rules Engine series - [What is a business rule engine in lending?](https://research.finbox.in/blog/sentinel-what-is-a-business-rules-engine-in-lending/) - [What Is a Credit Decisioning Platform? Definition, Core Components & How It Differs from LOS and BRE](https://research.finbox.in/blog/sentinel-what-is-a-credit-decisioning-platform/) - [Rule-Based vs ML-Based Credit Decisioning: Can BRE and ML Models Work Together?](https://research.finbox.in/blog/sentinel-rule-based-vs-ml-credit-decisioning/)