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Job Description
Description:
- Build and refine credit strategies for short-tenure, small-ticket unsecured personal loans.
- Work with Data Science to improve decision engines, scorecards, rules, and real-time underwriting models.
- Create sharp risk segmentation using behaviour, device signals, bureau data, fraud patterns, and portfolio trends.
- Analyse bounce rates, roll rates, early-bucket trends, and cohort behaviour to identify segments driving delinquencies.
- Share clear insights with Collections to boost pre-due and early DPD performance and improve cure rates.
- Track vintages, cohorts, and DPD curves to iterate policies, limits, and approval logic continuously.
- Strengthen fraud detection, identity checks, and thin-file underwriting frameworks to ensure clean approvals.
- Own policy governance, compliance, documentation, and alignment with RBI Digital Lending Guidelines.
- Stay ahead of market trends, competitor practices, and regulatory shifts to keep the risk engine future-ready.
Ideal Candidate:
- 2.5+ years in unsecured personal loan risk within a fintech or NBFC.
- Strong understanding of digital underwriting, policy design, and credit decisioning for short-tenure PL.
- Experience working with scorecards, rule engines, bureau data, device signals, and behavioural insights.
- Ability to work closely with Data Science, interpret large datasets, and convert insights into clear credit actions.
- Strong communication, cross-functional collaboration, and a proactive, analytical mindset.
Skills
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