Senior Fraud Risk Analyst

1 day ago

Ajmer, Rajasthan, India beBeeFraudRisk Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Job Title: Senior Fraud Risk Modeler(Credit Card)

Role Overview:

As a senior fraud risk modeler, you will be responsible for designing, developing, and maintaining models to detect and prevent fraud across various financial products and channels - primarily in the credit card domain.

  • Model Development & Validation: Build, implement, and validate fraud detection and prevention models using advanced statistical and machine learning techniques for credit card products.
  • Data Analysis & Interpretation: Analyze large-scale transactional and behavioral data to identify fraud patterns, assess model performance, and provide actionable insights.
  • Risk Assessment & Monitoring: Evaluate existing fraud controls, monitor fraud trends, and quantify exposure to evolving threats across digital and traditional channels.
  • Communication & Collaboration: Effectively present analytical findings and model performance to both technical and non-technical stakeholders, including fraud operations, compliance, and executive leadership.
  • Documentation & Reporting: Maintain comprehensive documentation of model development processes, risk assumptions, fraud detection frameworks, and compliance requirements.
  • Technical Skills: Strong proficiency in Python, SQL, and data science tools; familiarity with real-time decision engines and fraud detection platforms is a plus. (e.g., Actimize, Falcon, SAS).
  • Domain Expertise: Deep understanding of fraud domain expertise for debit card product.
  • Leadership & Teamwork: Ability to lead fraud analytics initiatives, mentor junior analysts or data scientists, and collaborate with risk, IT, and fraud operations teams.
  • Problem Solving: Proactively identify vulnerabilities in fraud strategies, recommend enhancements, and respond to emerging threats in a fast-paced environment.
  • Adaptability: Stay current with the latest fraud schemes, detection technologies, and regulatory developments impacting fraud risk management.

Key Qualifications:

  • Bachelor's or Master's degree in a quantitative discipline such as Statistics, Mathematics, Computer Science, Economics, Data Science, or a related field.
  • 6 to 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 modelling, 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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