Sr. Fraud Analyst

Here's how you will make an impact:

• Analyze fraud trends, attack patterns, account behavior, transaction activity, payment flows, model outputs, and other risk signals to identify emerging threats and control gaps
• Design, tune, test, and evaluate fraud rules, risk scoring strategies, detection logic, alerts, and decisioning controls to improve fraud prevention and detection outcomes
• Monitor fraud performance metrics, including fraud losses, fraud capture, false positives, manual review volume, customer friction, and control effectiveness, and recommend improvements based on measurable results
• Conduct root cause analysis on fraud events, escalations, anomalous activity, missed fraud, and control failures, translating findings into specific corrective actions
• Partner with Product, Engineering, Data Science, Analytics, Compliance, Customer Care, and Operations to implement fraud control enhancements and improve real-time detection capabilities
• Support the fraud capability roadmap by identifying opportunities to improve rules, scoring, AI/ML model performance, identity verification, behavioral signals, vendor tooling, automation, and workflows
• Prepare clear analysis, recommendations, business cases, and executive-ready summaries that explain fraud risks, tradeoffs, expected impact, and required actions
• Support testing, launch, monitoring, and optimization of new fraud tools, vendor capabilities, detection strategies, and process improvements
• Maintain SOPs, reporting routines, control documentation, and governance artifacts that support consistent fraud prevention and detection execution

You Thrive Here By Possessing the Following:

• 5+ years of experience in fraud prevention, fraud detection, fraud strategy, payments risk, digital identity, fintech, banking, e-commerce, tax, or financial services fraud programs
• Hands-on experience designing, tuning, testing, and evaluating fraud rules, risk scoring strategies, detection logic, alerts, fraud controls, or automated decisioning workflows
• Strong analytical capability using dashboards, Excel, SQL or data querying tools, model outputs, transaction trends, case analysis, and operational data to identify fraud risks and recommend actions
• Experience monitoring and improving fraud performance metrics such as fraud losses, fraud capture, false positives, manual review volume, operational efficiency, and customer friction
• Demonstrated ability to identify root causes, connect patterns across data sources, and translate complex fraud signals into specific control recommendations
• Experience partnering with Product, Engineering, Data Science, Analytics, Compliance, Customer Care, and Operations to implement fraud control improvements
• Working knowledge of fraud tools, vendor platforms, rules engines, risk scoring systems, alerting processes, workflow tools, or real-time decisioning environments
• Practical understanding of AI/ML fraud model outputs, model performance monitoring, feature evaluation, or data-driven decisioning in an operational environment