Senior Fraud Risk Analyst

We are building and scaling a high-performance consumer lending platform and are looking for a Fraud Risk Analyst to help protect the business from identity fraud, first-party fraud, and credit abuse.

This role sits at the intersection of fraud, credit, and analytics, and will directly impact early loss performance and portfolio quality.

You will be responsible for identifying fraud patterns, building detection strategies, and implementing controls that prevent bad actors from entering the portfolio.

This is a hands-on, high-impact role suited for someone who is analytical, detail-oriented, and biased toward action, not just case review.

You will work closely with Credit, Product, Operations and Engineering to ensure fraud risk is properly identified and separated from credit risk in decisioning.

This role is Addison, TX-based with a 4-day in-office requirement.

• →Analyze application and early performance data to identify fraud patterns, including synthetic identity, first-party fraud, and credit abuse.
• →Develop and implement fraud detection strategies, including rules, thresholds, and decisioning logic.
• →Monitor early performance (e.g., FPD, zero-pay accounts) to identify potential fraud-driven losses.
• →Distinguish fraud risk vs credit risk, improving approval quality and reducing early loss.
• →Evaluate and optimize third-party fraud tools and data sources (e.g., identity verification, device intelligence, consortium data).
• →Design and execute tests to evaluate fraud strategies and improve detection performance.
• →Work with Product and Engineering to implement fraud rules and ensure accurate execution in production systems.
• →Investigate emerging fraud trends and proactively recommend changes to controls and policies.
• →Collaborate with Operations or servicing teams to improve fraud identification post-origination.
• →Collaborate cross-functionally with other departments to ensure decisions align with business goals and risk appetite.

• Degree in Data Science, Applied Mathematics, Statistics, Economics, Computer Science or a related field
• 4–6 years of experience in fraud, risk, or analytics, preferably in fintech, lending, or financial services
• Strong analytical skills with experience using SQL, Python, Excel, or similar tools to analyze large datasets
• Understanding of key fraud types, including synthetic identity and first-party fraud and familiarity with fraud tools (i.e. identity verification, device fingerprinting, consortium data)
• Experience identifying fraud patterns or working with fraud detection strategies (i.e. credit washing etc.)
• Ability to translate analysis into clear actions (rules, controls, strategy changes) and exposure to A/B testing, experimentation frameworks, or champion/challenger strategies
• Passion for keeping your skills up to date and exploring new methodologies
• The ability to distill complex problems and analysis into a clear and concise narrative

• Experience in subprime consumer lending, fintech, payments, or another regulated financial services technology environment.
• Hands-on experience applying AI to fraud management