Fraud Strategy

Job description

The ideal candidate will bring strong technical expertise in SQL and Python, coupled with a deep understanding of the payment fraud industry. You will play a critical role in managing fraud losses across Consumer-to-Consumer (C2C) and Consumer-to-Business (C2B) payment flows, while simultaneously improving payment authorization rates and ensuring a frictionless customer experience. **Key Responsibilities:**   Domain Expertise: Proven experience in the Payments Industry, specifically dealing with Transactional Fraud, Digital Wallets, Payment Gateways, and E-commerce fraud   Fraud Strategy & Mitigation: Design, implement, and manage robust fraud prevention strategies for C2C (peer-to-peer) and C2B (merchant checkout) payment products, as well as Debit Card portfolios (covering both Card Present and Card Not Present transactions).   Authorization Optimization: Analyze transaction data to improve payment authorization rates across various checkout flows (both branded and unbranded/guest checkouts) while keeping fraud losses within risk appetite.   Customer Experience (CX): Balance aggressive fraud mitigation with user experience by minimizing false positives (false declines) and reducing unnecessary friction for legitimate customers.   Data-Driven Investigation: Investigate suspicious transaction patterns, account takeovers (ATO), and payment anomalies to determine root causes and develop proactive statistical models/rules.   System & Rule Development: Design and deploy fraud rules within decision engines and workflow systems. Build comprehensive monitoring reports to track the performance of fraud strategies. **Required Qualifications**   3 to 6 years of hands-on experience in fraud strategy, specifically within Fintech, Digital Payments, E-commerce, or Payment Gateways.   Demonstrated experience in designing and implementing fraud rules using transactional, behavioral, and third-party data.   Strong proficiency in:   SQL: Advanced querying, joins, aggregations, and performance tuning for massive transactional datasets.   Python: Data manipulation (Pandas, NumPy), exploratory data analysis, and basic modeling; ability to productionize or hand over code to engineering teams.   Strong analytical and problem-solving skills with the ability to translate data insights into actionable strategies that directly impact the bottom line.