Card Fraud Analyst Jobs (NOW HIRING)

Help protect millions of customers by transforming complex card transaction data into clear, actionable fraud insights. You’ll surface emerging fraud schemes, strengthen governance and oversight, and support high-impact investigations. Partnering across Fraud Operations, Risk, Compliance, and Technology, you’ll improve data quality and drive better fraud outcomes. If you thrive in data-heavy environments and want your analysis to directly reduce fraud risk, this role is for you.

As a Card Fraud Risk Oversight Associate in the Card Fraud Oversight team, you help protect customers and the firm by monitoring fraud activity across card products and turning high-volume transaction data into timely insights and effective actions. You will analyze trends, detect anomalies, and support investigations while helping ensure oversight practices meet regulatory and audit expectations. You’ll also contribute to stronger data governance, clearer reporting, and process improvements that measurably reduce fraud risk.

• Aggregatelarge volumes of card transaction and case data to monitor fraud performance across card products.
• Validate datasets and metrics to ensure accuracy, completeness, and consistency across reporting and oversight outputs.
• Analyze transaction patterns, anomalies, and behavioral signals to identify emerging fraud risks and evolving schemes.
• Develop and maintain dashboards and recurring reports to track fraud KPIs, case volumes, resolution rates, and key drivers.
• Conduct deep-dive analyses on fraud cases, cardholder behavior, and root causes to support targeted mitigation strategies.
• Partner with Fraud Operations, Risk, Compliance, and Technology to address data quality issues and strengthen end-to-end oversight.
• Support investigations by providing clear, evidence-based insights, data extracts, and analytical narratives for decision-makers.
• Prepare materials and documentation for regulatory, audit, and management requests, ensuring timely and accurate submissions.
• Identify process improvement opportunities through data-driven findings and recommend actionable controls or operational enhancements.
• Document data definitions, methodologies, controls, and lineage to uphold data governance standards and ensure repeatability.

• Minimum 3 years of experience working with large datasets in an analytics, risk, fraud, or financial services environment.
• Minimum 3 years of hands-on experience using data analysis tools such as SQL, SAS, Python, Excel, and/or Tableau (or comparable BI tools).
• Bachelor’s degree in Business, Finance, Economics, or a related field (or equivalent practical experience, where applicable).
• Demonstrated ability to detect trends and anomalies and translate analytical outputs into clear risk insights and actions.
• Strong problem-solving capability with high attention to detail, including reconciliation and data quality validation.
• Proven ability to communicate complex findings to non-technical stakeholders through concise storytelling and visual reporting.
• Experience with card products, fraud detection, payments, or comparable consumer finance risk domains.
• Ability to collaborate effectively with both business and technical partners to drive outcomes and resolve issues.
• Excellent written and verbal communication skills, including the ability to produce audit-ready materials.
• Strong emotional intelligence and proven ability to influence across teams without direct authority.
• Ability to work independently, manage multiple priorities, and deliver under tight deadlines.

• Experience analyzing card authorization, clearing, settlement data and understanding transaction lifecycles and fraud typologies.
• Proficiency building automated reporting pipelines and dashboards (e.g., SQL automation, Python workflows, Tableau dashboards).
• Familiarity with fraud case management processes and operational performance metrics (e.g., case aging, closure quality, recoveries).
• Experience supporting regulatory exams and internal/external audits, including strong documentation and controls discipline.
• Knowledge of data governance concepts (data lineage, definitions, controls, quality checks) and applying them in analytics workflows.
• Exposure to machine learning/advanced analytics concepts for fraud detection (feature engineering, model monitoring, alert tuning).
• Experience working with large-scale data environments (e.g., cloud data platforms, distributed datasets) and performance optimization.