Executes fraud analytics assignments from requirements gathering and data assessment through analysis, implementation support, measurement, and monitoring.
Develops, tests, and maintains offline fraud detections that generate actionable leads for investigative teams, with guidance on more complex efforts.
Monitors detection performance using measures such as precision, false-positive rates, alert volumes, loss exposure, and prevented or avoided impact.
Analyzes account, client, transactional, and case data to identify emerging fraud trends, anomalous behavior, common attributes, and fraud signatures.
Provides analytical and forensic support for fraud incidents, control gaps, emerging typologies, and other priority investigations.
Develops and maintains dashboards, recurring reports, loss reporting, benchmarking analyses, and fraud performance products.
Performs data quality checks and validates the completeness, accuracy, and reasonableness of data used in reporting and detections.
Documents analytical methodologies, assumptions, data lineage, testing results, and operating procedures in accordance with established standards.
Translates analytical findings into clear, actionable insights for fraud operations, risk partners, technology teams, and other stakeholders.
Collaborates with senior analysts and business partners to validate analytical approaches, resolve data issues, and deliver quality work products.
Partners with investigative teams to obtain disposition feedback and identify opportunities to improve detection effectiveness.
Contributes to cross-functional fraud initiatives, data modernization efforts, and evaluation of fraud tools or capabilities.
Participates in special projects and performs other duties as assigned.
Minimum of five years related work experience.
Undergraduate degree or equivalent combination of training and experience.
Intermediate SQL skills, including experience querying, joining, validating, and analyzing large datasets.
Working proficiency in Python or another analytical programming language used for data preparation, automation, statistical analysis, or detection development.
Experience developing and maintaining dashboards and reports in Tableau or a comparable visualization platform.
Experience supporting the development, testing, monitoring, or optimization of fraud detections, risk rules, anomaly-detection methods, or analytical models.
Working knowledge of analytical validation methods, data