Fraud Analyst Advisor at Pathward Financial

  • Company: Pathward Financial
  • Location: Remote | USA
  • Salary: $72K – $120K
  • Job type: full time
  • Workplace: remote
  • Posted: 2026-10-22

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Job description

We are a hybrid, remote-office company dedicated to growing our talent anywhere! We have onsite locations in: Sioux Falls, SD, Phoenix, AZ, Louisville, KY, Troy, MI, Franklin, TN, Easton, PA. At Pathward, we take tremendous pride in our purpose to create financial inclusion for all™. We are a financial empowerment company that works with innovators to increase financial availability, choice, and opportunity for all. We strive to remove barriers that traditional institutions put in the way of financial access, and promote economic mobility by providing responsible, secure, high quality financial products. We are a team of problem solvers and innovators who celebrate our differences and know that our unique perspectives make us stronger and well-positioned for success. We celebrate, and embrace, our team members through our *HUMBLE*HUNGRY*SMART approach, and we believe that we are strongest when we embrace the voices of our employees, customers, partners, and the communities we serve. About the Role: Within the Fraud Risk Management and Operations function, this role is specifically responsible for analyzing fraud-related data across alerts, cases, losses, and customer activity to identify emerging fraud trends, typologies, and control gaps. The role emphasizes fraud-centric reporting, including the design, production, and interpretation of fraud KPIs/KRIs, to support effective fraud detection, investigation, escalation, and overall program performance. What You Will Do: Analyze large, complex datasets to generate insights across credit performance, fraud trends, and portfolio risk, using descriptive, predictive, and prescriptive analytics, with a primary focus on fraud detection effectiveness, loss drivers, alert-to-case conversion, and emerging fraud typologies. Support development and monitoring of fraud detection models, rules, thresholds, and segmentation strategies, ensuring alignment with evolving fraud risks and typologies. Design and deliver fraud and risk reporting (KPIs/KRIs) aligned to BSA/AML and enterprise risk standards, ensuring, audit traceability, and clear linkage to control effectiveness, including reporting on fraud losses, alert volumes, investigation outcomes, SAR referral indicators, and operational effectiveness metrics across the fraud lifecycle. Translate complex data into actionable, executive-ready insights, while partnering cross-functionally to implement strategies, address control gaps, and enhance overall risk and fraud program performance, including identification of fraud control weaknesses, breakdowns in alerting or investigation processes, and opportunities to improve detection, escalation, and resolution outcomes. Partner with Fraud and BSA/AML Operations, Compliance, Risk, Product, and Technology teams to ensure fraud reporting aligns with regulatory expectations and supports effective decision-making, including SAR-related considerations where applicable Analyze fraud alert and case lifecycle data (intake, triage, investigation, disposition) to identify process inefficiencies, quality issues, and opportunities for automation or control enhancement. Other duties as assigned. What You Will Need: Bachelor's degree or equivalent education and work experience. Master's degree preferred. Typically requires a minimum of 8 years of related experience with a bachelor’s or equivalent. Experience in data analysis within financial services, fraud risk management, payments, or related domains preferred. Ability to handle large volume of data and derive actionable insights. Strong analytical and problem-solving skills, combined with solid business acumen. Strong written and communication skills with ability to explain complex insights to a range of audiences. Experience in the consumer credit industry is a plus. Experience with fraud analytics, fraud reporting, payments data, or financial crime data strongly preferred. Foundational understanding of fraud typologies (e.g., account takeover, first-party f

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