ABOUT THE ROLE
As a Lead Fraud Analyst at Happy Money, you’ll serve as a key individual contributor responsible for developing, analyzing, and optimizing our fraud risk strategy. Sitting within the Risk and Analytics team, you will own fraud risk analytics, fraud forecasting, metrics governance, and continuous improvement initiatives that enhance both fraud prevention and customer experience. You’ll work cross-functionally with Fraud Operations, Product, and Engineering to drive proactive, data-driven fraud detection strategies that help protect Happy Money and our lending partners at scale.
RESPONSIBILITIES
- Develop and implement Fraud strategies leveraging data, machine learning models and advanced analytics tools to mitigate fraud losses and maintain superior customer experience.
- Manage and optimize existing strategies effectively controlling risks due to first party and third-party fraud.
- Design A/B tests and understand the risk and customer experience trade-offs.
- Lead fraud forecasting and budgeting activities by modeling fraud losses, operational impacts, and fraud trend assumptions.
- Lead the efforts to constantly iterate and improve existing strategies while identifying additional automation opportunities.
- Continue to innovate by testing new data, analytics approaches and models.
- Monitor and analyze portfolio performance at a granular segment level on an ongoing basis. Identify trends and conduct root-cause analysis to isolate key performance drivers.
- Communicate findings and recommendations to the Risk Management and broader Prosper community.
- Maintain fraud losses within the allocated annual budget.
- Identify and evaluate new vendors / data sources and create business cases and present them to senior management.
- Partner with Product, Marketing, Engineering and External Vendor teams to implement strategies and monitor the strategy performance.
- Bring innovative/out of the box thinking to solve problems and make recommendations.
- Conduct ad-hoc analysis related to risk management, product, operations.
- Drive feedback loops using member complaints, bureau data, and operations insights to improve fraud detection performance.
- Provide subject matter expertise during fraud incidents, investigations, and root cause analyses.
ABOUT YOU
- 6+ years of experience managing credit risk and/or fraud investigations.
- Bachelor’s or Master’s degree in Business, Statistics, Mathematics, Computer Science, Engineering or Economics
- Strong analytical and problem-solving skills. Deep experience working with fraud detection strategies, rules tuning, and fraud modeling.
- Strong proficiency with SQL; Ability to analyze large, complex datasets and derive actionable insights
- Experience with fraud orchestration and decisioning tools (e.g., Oscilar, Socure, SentiLink) preferred.
- Strong understanding of fraud metrics, forecasting methods, and false positive/true fraud tradeoffs.
- Excellent problem-solving skills with the ability to synthesize complex data into actionable insights.
BONUS POINTS FOR
- Experience supporting machine learning-driven fraud strategies or hybrid rules/ML fraud systems.
- Familiarity with regulatory frameworks such as BSA/AML as they pertain to fraud risk.
- Prior experience in a high-growth fintech, lending, or digital banking environment.
BENEFITS & PERKS
- Medical, dental, and vision insurance options covered at 100% for you & family
- 401k Plan with a matching contribution
- Generous PTO
- Monthly Health and Wellness Days
- 12 Weeks Paid Family Leave
- Company-paid short & long term disability and life insurance
- Happy Money is a 100% fully distributed workforce
$135,000 - $155,000 a yearHappy Money is committed to a diverse and inclusive workplace. Happy Money is an equal opportunity employer and does not discriminate based on race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please email peopleops@happymoney.com.Apply for this job
