Card Fraud Risk Oversight Associate

Help protect millions of customers by transforming complex card transaction data into clear actionable fraud insights. Youll surface emerging fraud schemes strengthen governance and oversight and support high-impact investigations. Partnering across Fraud Operations Risk Compliance and Technology youll 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/anCard Fraud Risk Oversight Associatein theCard 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. Youll also contribute to stronger data governance clearer reporting and process improvements that measurably reduce fraud risk.

Job Responsibilities

• Aggregatelarge volumes of card transaction and case data to monitor fraud performance across card products.

• Validatedatasets and metrics to ensure accuracy completeness and consistency across reporting and oversight outputs.

• Analyzetransaction patterns anomalies and behavioral signals to identify emerging fraud risks and evolving schemes.

• Developandmaintaindashboards and recurring reports to track fraud KPIs case volumes resolution rates and key drivers.

• Conductdeep-dive analyses on fraud cases cardholder behavior and root causes to support targeted mitigation strategies.

• Partnerwith Fraud Operations Risk Compliance and Technology to address data quality issues and strengthen end-to-end oversight.

• Supportinvestigations by providing clear evidence-based insights data extracts and analytical narratives for decision-makers.

• Preparematerials and documentation for regulatory audit and management requests ensuring timely and accurate submissions.

• Identifyprocess improvement opportunities through data-driven findings andrecommendactionable controls or operational enhancements.

• Documentdata definitions methodologies controls and lineage to uphold data governance standards and ensure repeatability.

Required Qualifications Capabilities and Skills

• Minimum 3 yearsof experience working with large datasets in an analytics risk fraud or financial services environment.

• Minimum 3 yearsof hands-on experience using data analysis tools such asSQL SAS Python Excel and/orTableau(or comparable BI tools).

• Bachelors degree inBusiness Finance Economics or a related field (or equivalent practical experience where applicable).

• Demonstrated ability todetect trends and anomaliesand translate analytical outputs into clear risk insights and actions.

• Strongproblem-solvingcapability with high attention to detail including reconciliation and data quality validation.

• Proven ability tocommunicate complex findingsto non-technical stakeholders through concise storytelling and visual reporting.

• Experience withcard products fraud detection payments or comparable consumer finance risk domains.

• Ability to collaborate effectively with bothbusiness and technicalpartners to drive outcomes and resolve issues.

• Excellentwritten and verbal communicationskills including the ability to produce audit-ready materials.

• Strongemotional intelligenceand proven ability to influence across teams without direct authority.

• Ability to workindependently manage multiple priorities and deliver under tight deadlines.

Preferred Qualifications Capabilities and Skills

• Experience analyzingcard authorization/clearing/settlementdata and understanding transaction lifecycles and fraud typologies.

• Proficiency building automated reporting pipelines and dashboards (e.g. SQL automation Python workflows Tableau dashboards).

• Familiarity withfraud case managementprocesses and operational performance metrics (e.g. case aging closure quality recoveries).

• Experience supportingregulatory examsand internal/external audits including strong documentation and controls discipline.

• Knowledge ofdata governanceconcepts (data lineage definitions controls quality checks) and applying them in analytics workflows.

• Exposure tomachine learning/advanced analyticsconcepts 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.