Senior Product Manager - Payments Fraud & Risk Management

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.