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.