Founded in 2015 and based in Chicago, IL, privately held Braviant Holdings, LLC is a leading provider of tech-enabled consumer credit products that combine 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® — helping 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.
We are a lean team of approximately 40 people who move fast and hold ourselves accountable for real outcomes. Everyone here rolls up their sleeves — including this role.
- 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. Required
- 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.