Enterprise Risk - Fraud Risk Analytics & Technology - Principal; Second Line

Job Description

Playing an essential role in the U.S. economy, Fannie Mae is foundational to housing finance. Here, your expertise can help fuel purpose‑driven innovation that expands access to home ownership and affordable rental housing across the country. Join Fannie Mae to grow your career and help people find a place to call home.

As a leader within the Analytics & Modeling team in the Second Line of Defense (Enterprise Risk Management), you will lead the strategy and oversight of Fannie Mae’s fraud risk analytics and fraud‑related technology. You will provide authoritative independent challenge, advanced analytical insight, and credible oversight of the First Line’s fraud detection, prevention, and monitoring capabilities. You will shape the enterprise fraud analytics agenda, strengthen fraud data governance, and ensure our tools and methods align with risk appetite and supervisory expectations.

• Provide independent oversight and effective challenge of First Line fraud analytics, models, rules libraries, AI/ML algorithms, monitoring logic, and surveillance activities.
• Set and execute the Second Line fraud analytics oversight agenda, including thematic reviews, deep‑dive validations, and targeted examinations of model effectiveness and control design.
• Advise and brief the Head of Fraud Risk Oversight on systemic analytics weaknesses, data risks, technology gaps, and control failures requiring escalation.
• Drive fraud data governance standards (lineage, quality, completeness, accuracy, availability) critical to detection and reporting.
• Translate complex analytical findings into clear, decision‑useful insights for ERC, senior leadership, and the Board.
• Define the enterprise fraud analytics and technology strategy for the Second Line, aligned to fraud taxonomies, risk appetite, and emerging threats.
• Oversee development and challenge of advanced fraud analytics, including anomaly detection, behavioral analytics, network/graph analysis, segmentation, and specific risk indicators (e.g., employment fraud, authentication fraud, scams, business email compromise, impersonation of Fannie Mae).
• Evaluate, recommend, and oversee the implementation of fraud technology platforms (e.g., case management, detection engines, analytics environments) and the data pipelines that support them.
• Assess and challenge AI/ML explainability, fairness, bias, stability, and controls for fraud‑related analytics, in partnership with model risk management.
• Build and maintain risk reporting and dashboards for ERMC, senior leadership, the Board, and supervisory interactions.

• 8 years of deep expertise in fraud analytics, financial crimes data, and technology‑enabled fraud controls within large financial institutions or government regulated entities.
• Hands‑on experience building or overseeing fraud detection models and methods (e.g., risk indicators, segmentation, anomaly detection, typology analytics, network/graph techniques).
• Strong understanding of Second Line of Defense operating models and the roles of independent oversight, challenge, and escalation.
• Familiarity with fraud taxonomies, enterprise fraud risk frameworks, and fraud case management processes.
• Proficiency with Python, R, SQL, Spark, cloud analytics environments, data lakes, and fraud‑focused data pipelines.
• Experience with GRC/fraud platforms (e.g., Actimize, SAS Fraud, FICO, Verafin, or similar).
• Ability to evaluate AI/ML explainability, fairness, model risk, and control design for fraud analytics in partnership with model risk functions.
• Proven ability to craft executive‑ready communications and translate analytics into risk insights for senior leadership, ERMC, and the Board.

• Bachelor’s degree or equivalent.
• Advanced degree in analytics, data science, statistics, computer science, finance, or related field.
• Professional certifications such as CAMS, CFE, CRCM, FRM, or equivalent.
• Experience in housing finance/mortgage ecosystems and associated fraud typologies.
• Exposure to model risk management practices and regulatory interactions on analytics/technology…