Senior Fraud Risk Analyst
Braviant Holdings
Description
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 Crainâs 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.
WHAT YOUâLL BE DOING
- 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.
- Tagsanalystfinancialstrategysenioroperationshealthcareengineering
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About Braviant Holdings
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Interview Prep Guide
Preparation Strategy
To prepare for this role, review your experience in fraud risk analysis and stay up-to-date with emerging fraud trends and patterns. Practice analyzing sample data sets to identify potential fraud, and prepare examples of times when you identified and addressed complex fraud patterns. Additionally, review statistical methods for detecting fraud patterns, such as machine learning algorithms and data visualization techniques. Be prepared to discuss your experience working with cross-functional teams to implement fraud controls.
Likely Interview Rounds
- 1. Screening call~30 min
What to prep: Review your experience in fraud risk analysis, and be prepared to discuss your knowledge of emerging fraud trends and patterns.
- What experience do you have in fraud risk analysis?
- How do you stay up-to-date with emerging fraud trends and patterns?
- 2. Technical~60 min
What to prep: Review statistical methods for detecting fraud patterns, and practice analyzing sample data sets to identify potential fraud.
- How would you analyze application and early performance data to identify fraud patterns?
- What statistical methods would you use to detect synthetic identity, first-party fraud, and credit abuse?
- 3. Behavioral~60 min
What to prep: Prepare examples of times when you identified and addressed complex fraud patterns, and be ready to discuss your experience working with cross-functional teams.
- Can you describe a time when you identified a complex fraud pattern and developed a detection strategy?
- How do you collaborate with cross-functional teams to implement fraud controls?
Most Likely Questions
- What experience do you have in fraud risk analysis?
- How do you stay up-to-date with emerging fraud trends and patterns?
- How would you analyze application and early performance data to identify fraud patterns?
- What statistical methods would you use to detect synthetic identity, first-party fraud, and credit abuse?
- Can you describe a time when you identified a complex fraud pattern and developed a detection strategy?
Common Pitfalls
- Lack of experience in fraud risk analysis
- Inability to stay up-to-date with emerging fraud trends and patterns
- Insufficient knowledge of statistical methods for detecting fraud patterns
Free Prep Resources
- • Kaggle
- • DataCamp
- • Coursera - Fraud Detection Course