Fraud Strategy Manager

About the role

You will architect and operate the entire fraud strategy for our onboarding funnel, owning the frameworks that decide which applicants move forward, which receive heightened scrutiny, and which are declined. This role requires you to synthesize complex data streams, operational workflows, and regulatory constraints into a coherent decisioning philosophy that protects the business without compromising growth. You will translate ambiguous risk signals into concrete policies and execution plans that balance fraud prevention with a seamless user experience. You will be the central strategist for how we evaluate new applicants as we scale, ensuring our practices remain robust as product offerings and attack vectors evolve. You will own the design, implementation, and continuous optimization of controls across the applicant lifecycle from initial application to final decision. You will act as the bridge between technical tooling, data science models, and frontline operations to ensure strategy is actionable and measurable. You will be accountable for the integrity of our decisions and the clarity of our reasoning to executive stakeholders and partners.

Key facts

What you'll do

• Define the logic and thresholds for routing applicants into manual review, balancing fraud risk against approval rates and customer experience.
• Own the manual review program end-to-end: queue prioritization, SLA design, analyst tooling, and case escalation paths.
• Evaluate the effectiveness of tools used in manual review; identify gaps and advocate for new capabilities that help analysts make faster, higher-quality decisions.
• Track manual review outcomes rigorously: analyst decisions, approval/decline rates, reversal rates, downstream fraud on reviewed accounts, and false positive costs.
• Build structured feedback loops between review outcomes and upstream rules triggers to drive continuous policy refinement.
• Lead the strategy for incorporating bank account data into onboarding decisions, leveraging signals such as account tenure, balance history, income patterns, and return/NSF activity.
• Operationalize device intelligence and behavioral signals to strengthen identity and fraud detection at the top of the funnel.
• Develop a framework for using partner data: loyalty engagement, transaction history, tenure signals as supplementary fraud indicators in co-brand card programs.
• Evaluate new data sources and vendors on an ongoing basis; build a rigorous test-and-learn methodology to validate signal lift before production deployment.
• Own the fraud rules framework for onboarding: design, test, implement, and continuously tune rules across identity, velocity, device, funding, and behavioral dimensions.
• Partner with data science to define feature requirements, evaluate model performance, and translate model outputs into operational policy.
• Document all policy decisions clearly, including the tradeoffs made at each threshold.
• Define and own the KPI framework for onboarding fraud: fraud rate by vintage and partner, manual review rate, auto-decisioned bad rate, tool efficacy, and cost-per-review.
• Conduct regular portfolio reviews to surface emerging fraud patterns, track loss trends, and assess detection performance.
• Build reporting to enable real-time monitoring, trend identification, and rapid policy response.
• Partner with Product and Engineering to translate fraud strategy into system requirements and influence roadmap prioritization.
• Work with Compliance and Legal to ensure onboarding controls meet BSA/AML, Red Flags Rule, ECOA, and UDAAP requirements.
• Collaborate with Customer Operations to manage edge cases, decision appeals, and applicant escalations.

Requirements

• 5-10 years of experience in fraud strategy, identity risk, or credit risk at a financial institution, fintech, or payments company.
• Deep expertise in onboarding fraud - synthetic identities, identity manipulation, first-party fraud vectors, and deposit/funding fraud.
• Hands-on experience with bank data providers in a fraud or credit risk context.
• Familiarity with device intelligence and behavioral fraud platforms.
• Strong SQL skills and experience using data to build and evaluate fraud rules, track performance, and identify emerging patterns.
• Excellent communication skills - you can translate complex fraud tradeoffs into clear recommendations for executives, partners, and operators.
• Comfort operating in environments with high ambiguity and competing priorities, where frameworks and judgment are more valuable than rigid playbooks.
• A bias for ownership and scrappy execution, with the ability to drive initiatives across teams without formal authority.
• Intellectual curiosity and rigor - you want to understand not just what works, but why it works and how to make it better.
• Strong judgment and discretion when handling sensitive applicant data and risk decisions.
• Comfort working with regulated financial services products and an understanding of the implications of compliance constraints.