Application Fraud Analyst (Identity Fraud Focus)

Application Fraud Analyst (Identity Fraud Focus) Location: Atlanta Type: Full-time Level: Senior Analyst Role Overview We're looking for an Application Fraud Analyst to detect, investigate, and prevent fraud at the point of application/onboarding, with a strong emphasis on identity fraud. You'll use SQL and Python or SAS to build analyses, identify emerging fraud patterns, optimize rules and strategies, and communicate actionable insights to cross-functional partners. What You'll Do Monitor and analyze application/onboarding activity to identify fraud patterns, anomalies, and emerging attack vectors (synthetic ID, identity theft, first-party fraud, ATO indicators). Perform end-to-end investigations: develop hypotheses, pull and join data via SQL, and validate findings using Python/SAS analysis. Build and maintain fraud analytics (dashboards, metrics, performance monitoring) to measure fraud rates, approval impacts, and operational outcomes. Recommend and implement strategy changes (rules, thresholds, segmentation, policy updates) that balance risk control with customer experience and growth. Partner closely with underwriting, operations, product, engineering, and compliance to operationalize fraud controls and improve workflows. Create clear written summaries and present findings to leadership—translating complex analytics into decisions and next steps. Support experimentation (A/B tests, challenger strategies), post-implementation monitoring, and ongoing tuning. Required Qualifications Strong analytical thinking with the ability to structure ambiguous problems and drive to clear decisions. Advanced SQL skills (complex joins, window functions, aggregations, query optimization). Proficiency in Python or SAS for data analysis and automation (e.g., pandas/NumPy or SAS procedures/macros). Excellent communication skills (written and verbal), including the ability to explain risk tradeoffs to both technical and non-technical audiences. Experience investigating fraud, risk, or identity-related anomalies using data and case evidence. Bachelor's degree in a quantitative field (or equivalent practical experience). Preferred Qualifications (Ideal Candidate) Direct experience in identity fraud / application fraud (e.g., synthetic identity, identity theft, document/IDV signals, device/email/phone risk, bureau/alt-data patterns). Experience with fraud tools and signals: device intelligence, email/phone reputation, IP/proxy/VPN indicators, behavioral signals, document verification, watchlists/KYC. Familiarity with model/rule performance monitoring (precision/recall, approval rate, loss rate, drift, stability, calibration). Experience working with large datasets in cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) and BI tools (Power BI/Tableau) is a plus. What Success Looks Like Detect and mitigate new fraud patterns quickly with data-backed strategy recommendations. Improve fraud loss outcomes while protecting legitimate approvals and customer experience. Deliver high-quality, well-documented analyses and clear executive-ready narratives. Build scalable SQL/Python/SAS workflows that reduce manual effort and speed up decisioning.