Senior Fraud Risk Analyst

Braviant Holdings•Dallas, TX

1d•Hybrid

About The Position

At Braviant, we believe in hiring great talent and offering them the flexibility to achieve great results unbounded by geography. Braviant is offering a fully remote option for anyone in the U.S. who wants to join our team and help us grow. We also have an office space in the heart of downtown Chicago for those who prefer to get out of the house and collaborate with some colleagues in person. Who we are: Braviant is a leading provider of tech-enabled credit products that combines breakthrough technology and cutting-edge machine learning to transform how people access credit online. Our next-generation approach to lending reduces credit barriers and creates a Path to Prime to help millions of underbanked consumers build credit history, reduce their cost of borrowing, and take control of their personal finances. Braviant has been named multiple times to the Inc. 5000 list of fastest growing private companies and has been recognized as a Best Place to Work by Crain’s Chicago, BuiltIn Chicago and American Banker. POSITION SUMMARY 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.

Requirements

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 intelligence, 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

Nice To Haves

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

Responsibilities

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.