Piramal Finance Scales AI Across Lending Lifecycle
Piramal Finance is architecting an AI-native operating model across its lending lifecycle, embedding agentic decision systems, product-specific scorecards, and governance-by-design, according to Chief Data & Analytics Officer Markandey Upadhyay. With 145+ live AI use cases, AI-assisted processing of ~8 billion tokens per month, serving over 5 million customers and US$7 billion AUM, the firm reports improved approvals, reduced fraud, faster underwriting and a projected 25-basis-point structural opex benefit.
Key Points
- 1Deploys agentic decision systems and 145+ live AI use cases across underwriting and servicing
- 2Enhances risk separation using product-specific scorecards and a patent-pending leverage risk model
- 3Requires governance-by-design, human-in-the-loop, and continuous monitoring to scale AI safely and measurably
Scoring Rationale
Strong, actionable company deployment with measurable outcomes drives score, but confinement to one NBFC limits broader industry generalizability.
Practice interview problems based on real data
1,625 SQL & Python problems across 15 industry datasets — the exact type of data you work with.
Try 250 free problems
