Sr Fraud Data Analyst, P2P & Cash Advance
Company: Sezzle
Location: Minneapolis, MN (Remote)
Type: Full-time
Remote: Yes
Posted: 2026-09-14
About this role
About Sezzle:
With a mission to financially empower the next generation, Sezzle is revolutionizing the shopping experience beyond payments, blending cutting-edge tech with seamless, interest-free installment plans that make shopping smarter and more accessible. We’re not just transforming payments; we’re redefining how people discover, interact with, and purchase the things they love while driving real impact on merchant sales through increased conversions and higher order values. As we continue to shape the future of fintech and retail, we’re building an innovative, dynamic team passionate about creating more than just a transaction but a truly unique shopping journey. If you’re excited about pushing boundaries in tech and delivering a game-changing experience for consumers and merchants alike, come join us at Sezzle and help create the future of shopping!
About the Role:
As a Sr Fraud Analyst specializing in Peer-to-Peer (P2P) and Cash Advance products, you will be on the front lines of protecting Sezzle and our customers from financial loss and malicious activity. You will leverage data analytics, investigation tools, and industry knowledge to identify, mitigate, and prevent complex fraud schemes, including Account Takeover (ATO), friendly fraud, synthetic identities, and social engineering scams.
The ideal candidate is naturally curious, highly analytical, and thrives in a fast-paced environment where fraud trends evolve rapidly.
What You'll Do:
- Transaction Monitoring & Investigation: Conduct daily reviews of high-risk P2P transfers and cash advance requests to identify fraudulent activity. Investigate suspicious accounts and take appropriate action (e.g., freezing accounts, declining transactions).
- Trend Analysis & Rule Optimization: Analyze fraud patterns and emerging trends to identify gaps in current defenses. Work closely with the Risk Strategy team to recommend and test new fraud rules and machine learning model features....