Fraud Strategy Data Analyst

At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed.  We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever-growing range of opportunities to discover what makes you thrive at every stage of your career. Try new things, learn new skills and discover what you excel at—all from Day One.

The Fraud Strategy Data Analyst provides enterprise-level analytical insight into fraud decisioning performance, fraud risk reduction, customer experience, operational efficiency, and financial outcomes across deposits, digital, payments, authentication, and the end-to-end account lifecycle. Through advanced, data-driven analysis, this role evaluates alert performance, decision accuracy, queue dynamics, loss drivers, customer friction points, operational inefficiencies, and emerging fraud risks to identify opportunities that improve fraud outcomes and business performance. By developing and applying consistent fraud tradeoff, loss avoidance, and decisioning performance frameworks, the analyst translates complex analytics into actionable, decision-ready insights that enable leadership to optimize fraud controls, improve detection effectiveness, reduce customer friction, enhance workload management, and prioritize investments based on measurable enterprise impact. The role does not design or implement fraud rules or strategies but provides the analytical foundation that informs enhancements to fraud decisioning, mitigation processes, and enterprise fraud initiatives through close partnership with Fraud Strategy, Risk, Operations, Digital, Technology, and AI/ML teams.

Leverage advanced technical proficiency in SAS/SQL/Python, and related analytical tools to extract, transform, and analyze large-scale fraud data, delivering scalable, data-driven insights that support fraud decisioning, tradeoff analysis, performance measurement, and strategic business decision-making. Analyze enterprise fraud performance across deposits, digital, payments, authentication, and account lifecycle domains to identify material loss drivers, customer friction, operational impacts, emerging risks, and opportunities to improve fraud outcomes. Evaluate fraud decisioning performance, alert effectiveness, decision outcomes, false positive rates, workflow execution, and queue dynamics to identify performance gaps, inefficiencies, and optimization opportunities. Assess end-to-end fraud operations, including alert lifecycle performance, queue throughput, review capacity, case-handling efficiency, and workload distribution to improve operational effectiveness and scalability. Develop and apply fraud tradeoff, loss avoidance, and performance measurement frameworks that quantify the impact of fraud controls across fraud loss reduction, customer experience, operational efficiency, and financial outcomes. Engineer, maintain, and enhance methodologies for measuring fraud benefit, losses avoided, operational costs, customer friction, false positives, and overall business impact using transparent, audit-ready approaches. Conduct advanced and ad hoc analytics, root-cause investigations, and performance assessments to evaluate fraud controls, identify emerging threats, uncover inefficiencies, and support optimization or decommissioning decisions. Monitor fraud trends, alert behavior, decision outcomes, and operational performance to identify emerging risks, changing fraud patterns, anomalies, and degradation in control effectiveness. Quantify expected benefits, costs, risks, operational impacts, and tradeoffs associated with fraud initiatives, tools, models, and strategic enhancements to support investment prioritization and business case development. Partner with Fraud Strategy, Fraud