Fraud Prevention Analyst
- Vancouver, BC, Canada
Full-time
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Job details
- Job type
- Full-time
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- Posting language
- English
- Posted
Original job description
About the Role
Punt is entering an exciting new phase of growth, with new products and verticals launching alongside our existing business. With that growth comes new revenue streams, new fund flows, new systems, and plenty of interesting finance problems to solve.
We are seeking a detail-oriented Fraud Prevention Analyst to join our Risk team, based on-site at our Vancouver head office. This role is critical to protecting our online platform from fraudulent activity, minimizing financial losses, and ensuring a safe experience for our customers. This position requires close collaboration with our Customer Support, Product, and Engineering teams. The ideal candidate combines strong quantitative skills with 2–4 years of hands-on experience with fraud prevention, chargeback mitigation in electronic payments and data analysis.
What You'll Do
- Monitor transactions in real time to identify, investigate, and mitigate fraudulent activity
- Analyse chargeback and large datasets to detect emerging fraud patterns and trends
- Develop, test, and refine fraud detection rules and models with moderate autonomy
- Manage and resolve chargebacks, disputes, and payment reconciliation issues end-to-end
- Collaborate closely with Customer Support, Product, and Engineering teams to improve fraud controls and customer experience
- Prepare regular reports on fraud metrics, loss trends, and key risk indicators for leadership
- Stay current on payment fraud trends, regulations, and industry best practices
- Identify and help implement process improvements to reduce false positives and fraud losses
- Mentor junior analysts or support onboarding as needed
What You Bring
- Bachelor's degree in Mathematics, Statistics, Engineering, Computer Science, or a related quantitative field
- Experience with fraud detection platforms or tools
- 2–4 years of experience working with electronic payments within North America, including chargeback processes and dispute resolution
- Demonstrated experience analyzing and working with large datasets
- Strong critical thinking and problem-solving skills, with the ability to work with moderate independence
- Excellent attention to detail and sound judgment in ambiguous situations
- Strong communication skills, with the ability to translate data insights into actionable recommendations for both technical and non-technical stakeholders
- Must be able to work on-site at our Vancouver office
- Legally entitled to work in Canada
Nice to Have
- Familiarity with machine learning concepts applied to fraud/risk modelling
- Prior experience in e-commerce specifically
- Hands on experience with SQL, Python, R, or similar tools
More about this job
Responsibilities
Monitor transactions, investigate and mitigate fraud, analyze chargebacks and large datasets, and develop and refine fraud detection rules and models. Resolve disputes and payment reconciliation issues, report on risk metrics, collaborate across teams to improve controls, and support process improvements and junior analyst onboarding.
Requirements
Requires a bachelor's degree in a quantitative field and 2–4 years of experience with North American electronic payments, chargebacks, and dispute resolution. Candidates should have experience with fraud detection tools and large datasets, strong analytical judgment and communication skills, and be legally entitled to work in Canada and able to work on-site in Vancouver.
Skills
- Fraud Prevention
- Fraud Detection
- Chargeback Mitigation
- Dispute Resolution
- Electronic Payments
- Data Analysis
- Fraud Pattern Analysis
- Fraud Detection Rules and Models
- Payment Reconciliation
- Risk Reporting
- Critical Thinking
- Problem-Solving
- Communication
- Machine Learning
- SQL
- Python
Visa sponsorship
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Categories
- Finance & Accounting
- Data & Analytics
- Security & Safety
- Technology
Keywords
- Fraud Prevention
- Fraud Detection
- Electronic Payments
- Chargebacks
- Dispute Resolution
- Payment Reconciliation
- Transaction Monitoring
- Fraud Patterns
- Fraud Detection Rules
- Fraud Models
- Risk Analysis
- Data Analysis
- Large Datasets
- Fraud Metrics
- Loss Trends
- Key Risk Indicators
- False Positives
- Customer Support
- Product
- Engineering
- Machine Learning
- Risk Modelling
- E-Commerce
- SQL
- Python
- R
- North American Payments