About the Role:

We are looking for a Senior Product Manager - Fraud & Risk to build and scale products that protect customers and the business from fraud across UPI, BillPay, Gift Cards and Loyalty.

You will own the product charter across real-time fraud detection, prevention, decisioning, investigation and recovery, while ensuring legitimate transactions continue to flow smoothly.

The role involves working at high transaction volumes and making data-led trade-offs between fraud losses, false positives, approval rates and customer experience.

What You'll Do:

- Own the product strategy and roadmap for fraud and risk management across UPI, BillPay, Gift Cards and Loyalty.

- Build and scale real-time fraud detection, prevention and decisioning capabilities.

- Translate fraud patterns into rules, risk signals, model features and real-time decisioning logic with Data Science and Engineering.

- Build capabilities for fraud investigation, case management and recovery.

- Partner with Analytics to define risk metrics and develop Early Warning Systems.

- Evaluate and integrate third-party fraud/risk vendors and data providers.

- Analyse transaction patterns, anomalies and root causes to identify emerging fraud risks.

- Balance fraud losses, false positives/negatives, approval/redemption rates, operating costs and customer experience.

- Work closely with Data Science, Data Engineering, Engineering, Analytics, Risk and Operations teams to deliver scalable solutions.

What We're Looking For:

- 6-10 years of Product Management experience, preferably building complex, data-intensive products.

- At least 1+ year of experience in fraud, risk, payments risk, trust & safety, refunds/abuse, account security or a closely related area.

- Strong experience with payments and real-time transaction systems, ideally within India's fintech ecosystem.

- Understanding of fraud detection, risk decisioning and transaction monitoring.

- Strong analytical skills with an ability to investigate patterns, anomalies and root causes using data.

- Familiarity with loss rates, false positives/negatives, approval rates, precision and recall.

- Strong stakeholder management skills and the ability to influence Engineering, Data Science and Analytics teams without direct authority.