
Elevate Fraud Detection with Advanced Statistical Techniques
2 days ago
As a Senior Fraud Risk Modeler specializing in credit card fraud, you will design, develop and maintain models to detect and prevent fraud across various financial products and channels.
The ideal candidate will analyze emerging fraud trends, assess fraud risk and support fraud prevention strategies while clearly communicating insights to stakeholders and collaborating with cross-functional teams to enhance fraud detection capabilities and reduce fraud losses.
- Key Responsibilities:
- Design, develop and implement advanced statistical and machine learning techniques for credit card products.
- Build and validate fraud detection and prevention models to minimize false positives and maximize true positives.
- Develop deep analytical skills to extract actionable insights from large-scale transactional and behavioral data.
- Collaborate with cross-functional teams to enhance fraud detection capabilities and reduce fraud losses.
Requirements:
- Bachelor's or Master's degree in a quantitative discipline such as statistics, mathematics, computer science, economics, data science or a related field.
- 6-7 years of hands-on experience in a banking environment, specifically in developing, implementing and maintaining fraud risk models across products and channels.
- Proven experience in fraud analytics and modeling, including transaction monitoring, real-time detection and rules-based or machine learning-driven approaches.
- Strong programming skills in Python, SQL, R or SAS for model development, data manipulation and automation.
- Proficiency with machine learning and statistical techniques such as decision trees, gradient boosting, clustering, anomaly detection and neural networks.
- Good understanding of how linear regression and XGBoost work, including their assumptions, strengths and limitations.
- Solid understanding of fraud typologies and fraud prevention technologies.
- Demonstrated ability to work with large and complex datasets, conduct deep-dive analytics and extract actionable insights.
- Excellent communication and stakeholder management skills, with the ability to clearly articulate model logic, assumptions and outcomes to non-technical audiences.
- Experience performing fraud model validation, performance monitoring and tuning processes to ensure effectiveness and compliance.
- Strong problem-solving skills to address emerging fraud risks, data quality challenges and operational issues.
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