Sr. Fraud Analyst

The Senior Analyst, Fraud Prevention & Detection is a hands-on fraud risk professional responsible for identifying fraud patterns, evaluating controls, and improving prevention and detection strategies across digital customer and payment experiences. This role analyzes account activity, transaction behavior, payment flows, model outputs, case trends, and other risk signals to identify emerging threats and recommend practical control improvements.

The Senior Analyst partners with Product, Engineering, Data Science, Compliance, Customer Care, and Operations to improve rules, scoring, decisioning, alerting, vendor tools, automation, and real-time detection capabilities. This role is accountable for delivering clear analysis, actionable recommendations, and measurable improvements in fraud outcomes, including reduced fraud losses, stronger fraud capture, lower false positives, reduced manual review volume, improved operational efficiency, and balanced customer experience. The ideal candidate brings strong fraud expertise, analytical rigor, urgency, and the ability to turn ambiguous fraud signals into clear actions that strengthen the business.

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