Fraud Strategist

Job description

We are seeking a seasoned Fraud Strategy — Network (Network-level fraud prevention Authentication, scoring & cross-issuer signal) to design and execute fraud prevention strategies at the network level — spanning authorization fraud, account takeover patterns observed across issuers, application/synthetic ID fraud, and merchant-side fraud. The role partners with data science on network fraud scoring services, contributes to authentication strategy (3DS, tokenization), and owns KPIs around fraud loss rates, false-positive rates, and authorization impact. Key Responsibilities   Analyse emerging fraud trends across the network — cross-issuer attack patterns, merchant-side fraud rings, BIN attacks, enumeration, and synthetic identity flows.   Develop, tune, and monitor network-level fraud rules and scoring frameworks; partner with data science on model deployment, drift monitoring, and retraining cadence.   Contribute to authentication strategy — 3DS frictionless flow, CAVV reuse, MOTO codes, multi-token device, and VAAM-type initiatives.   Own fraud KPIs: fraud loss rate, false-positive rate, approval-rate impact, and downstream chargeback metrics.   Coordinate with issuer and acquirer fraud counterparts where ecosystem-level intervention is needed. Must-Have Experience   3–6+ years in fraud strategy at a card network, large issuer, acquirer, or payments fintech.   Strong SQL and Python; comfort with fraud rule engines, scoring frameworks, and large transaction datasets.   Working knowledge of 3DS, EMV, tokenization, and authentication standards. Nice to Have   Prior exposure to issuer/acquirer/network fraud defense programs.   Hands-on with behavioural biometrics, device intelligence, or graph-based fraud detection.