Highly Experienced Senior Credit Card Fraud Risk Analyst

1 day ago

Mangalore, Karnataka, India beBeeRisk Full time ₹ 1,20,00,000 - ₹ 2,00,00,000
Senior Fraud Risk Modeler Job Opportunity

This role involves designing and developing models to identify and prevent financial crime across multiple products and channels, primarily focusing on credit card transactions.

You will analyze emerging trends in fraudulent activity, assess the associated risks, and collaborate with various teams to enhance fraud detection capabilities and reduce potential losses.

Key Responsibilities:

  • Fraud Detection Model Development: Create and implement advanced statistical and machine learning techniques for identifying and preventing fraudulent activities within credit card products.
  • Data Analysis & Interpretation: Analyze large datasets to identify patterns indicative of fraudulent behavior, assess model performance, and provide actionable insights.
  • Risk Assessment & Monitoring: Evaluate existing controls, monitor trends in fraudulent activity, and quantify exposure to evolving threats across digital and traditional channels.
  • Communication & Collaboration: Effectively present analytical findings and model performance to stakeholders, including fraud operations, compliance, and executive leadership.
  • Documentation & Reporting: Maintain detailed documentation of model development processes, risk assumptions, fraud detection frameworks, and compliance requirements.

Requirements:

  • Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, economics, data science, or a related discipline.
  • 6-7 years of hands-on experience in a banking environment, specifically in developing and maintaining fraud risk models across products and channels.
  • Proven expertise 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.
  • Solid understanding of linear regression and XGBoost principles, including their assumptions, strengths, and limitations.
  • Able to work effectively with complex datasets, conduct in-depth 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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