Sr. Fraud Analyst
Betterment
| Company | Betterment |
| Category | Data & Analytics |
| Location | Betterment HQ - New York City |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Senior |
| Salary | Not stated by the employer |
| Posted | 6 Jul 2026 |
| Last verified | 9 Aug 2026 |
| Source | Employer ATS (greenhouse) |
Description
About Betterment
Betterment is a leading, technology-driven financial services company that offers investing, savings and retirement solutions for retail investors and investment advisors as well as financial wellness solutions, including a 401(k) for small and medium-sized businesses. Our team is passionate about our mission, to empower people to build wealth with confidence and ease. We’re headquartered in NYC and offer hybrid NY-based positions (four days/ week in-office, with no required office days during the summer and winter holidays).
About the Role
Betterment’s Risk Operations team plays a critical role in protecting our platform and customers from fraud and financial crime. As a Senior Fraud Analyst, you will be the analytical owner of our customer risk rating, KYC, and fraud monitoring models. You will use data to shape how we detect, prevent, and respond to emerging fraud trends.
You will partner closely with Data, Compliance, and Product to design controls, build monitoring, and regularly advise leadership on model performance, threats, and residual risk.
This role is based out of our NYC office. Below we've reflected the base salary range for this position. Actual salaries may vary depending on factors including but not limited to location, experience, and performance. The range listed is just one component of Betterment’s total compensation package for employees.
New York City: $135,000 - $160,000
This job may also be eligible for variable compensation in the form of a company incentive bonus.
A Day in the Life
Manage Fraud rule sets and controls
Set up and perform continuous rule performance monitoring
Utilize data analytics to refine rules and reduce false/positives
Design new rules to address emerging fraud patterns and detect suspicious activity
Identify and remedy control gaps
Influence architecture builds and data integration
Orchestrate fraud monitoring through full customer journey across payment rails, funding flows, logins, and behavioral signals.
Strengthen case management process
Partner with Engineering for data harvesting, analytics and rule implementation
Lead cross‑functional projects from problem definition through implementation and post‑mortem, ensuring stakeholders are aligned on goals, timelines, and outcomes.
Identify, implement and assess vendor solutions
Own customer risk and KYC models
Maintain and enhance customer risk rating, signup, and related fraud scoring models.
Define clear model performance metrics (e.g., detection rate, false positive rate, lift) and establish monitoring to track drift over time
Partner with Customer Experience team
To identify friction points, customer abandonment, and true cost of fraud prevention
Reduced account locks, allow faster money movements, and lower ID request rates while not increasing fraud
Increase customer greenlight passes and ensure manual reviews provide strong controls
Build monitoring and reporting
Partner with Data/Analytics to build dashboards that surface fraud KPIs, trends, model health, and control coverage for a variety of stakeholders (Ops, Compliance, Product, Leadership).
Create recurring reporting packages for FraudCo and Risk leadership, translating complex analysis into clear recommendations.
What We’re Looking For:
Experienced Fraud Professional: 3–5+ years in fraud prevention, analytics, and financial crime (fintech, banking, brokerage, or payments). Must have direct experience with fraud patterns/controls (e.g., ATO, ACH/transfer, card fraud, KYC/KYB, synthetic identities).
Data-Driven & Technical: Advanced SQL is required. Comfortable framing analytical questions, designing experiments, and interpreting model/rule performance.
Familiarity with Fraud ecosystem (solutions, capabilities, challenges)
Model/Control-Oriented: Understands fraud stack components (risk mo