We are looking for a Head of Risk to build an underwriting engine that safely approves Gen Z customers often declined by traditional lenders, operating at the speed of a consumer app. You will be instrumental in replacing current decision-making processes with a robust, owned, and explainable scorecard, leveraging alternative data and on-platform behavioural signals to maximise approval rates within acceptable NPA bands. This role is critical for the company's growth, focusing on innovative risk assessment and management for a new generation of consumers.
Requirements
Key Responsibilities
Build a clean, owned, and explainable application scorecard to replace current credit decisioning processes, independent of partner NBFC overlays.
Ingest alternative data via the Account Aggregator framework, including parsing bank statements, analysing UPI transaction patterns, merchant categories, average balance velocity, and salary credit regularity.
Utilise device quality, PIN code vintage, and telecom data as proxies for income and stability.
Incorporate on-platform behavioural signals such as browsing depth, Wishlist-to-purchase conversion, and time-on-app before application.
Implement risk-tiered approvals, aiming to maximise approval rates within an acceptable NPA band.
Build the infrastructure to capture, store, and model repayment behaviour data for the proprietary Behavioural Credit Bureau.
Develop a feature store to make behavioural signals available to the scoring engine in real time.
Own the model monitoring stack, including drift detection, cohort performance tracking, and vintage analysis.
Develop the Limit Graduation Engine logic for progressive credit journeys, managing limits from small to large tickets automatically and at scale.
Incorporate repayment timeliness scoring, purchase frequency, category migration signals, and external signals via AA refresh into the limit graduation logic.
Implement automated limit upgrade triggers balanced against NPA guardrails.
Develop a predictive pre-delinquency model to flag accounts likely to miss payments based on behavioural changes.
Design and implement empathetic, digital-first early outreach strategies for pre-delinquency intervention.
Define escalation logic that preserves brand relationships during stress events.
Establish closed-loop feedback mechanisms to analyse intervention effectiveness across customer segments and delinquency stages.
️ Required Skills
Credit Risk, Underwriting, Data Analysis, Machine Learning, Python, SQL, Account Aggregator Framework, RBI Digital Lending Guidelines, Explainable AI, Model Monitoring, Feature Engineering
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