Senior Financial Crime Analyst

3 days ago

Hubli, Karnataka, India beBeeFraud Full time ₹ 1,80,00,000 - ₹ 2,50,00,000

Job Role: Senior Fraud Risk Modeler

Experience: 5+ Years

A senior fraud risk modeler designs, develops and maintains models to detect and prevent fraud across various financial products and channels. They analyze emerging fraud trends, assess fraud risk, and support fraud prevention strategies while communicating insights to stakeholders and collaborating with cross-functional teams to enhance fraud detection capabilities and reduce fraud losses.

Main Responsibilities:

  • Fraud Detection and Prevention: Develop and validate fraud detection models using advanced statistical and machine learning techniques for credit card products.
  • Data Analysis and Interpretation: Analyze large-scale transactional and behavioral data to identify fraud patterns, assess model performance, and provide actionable insights.
  • Risk Assessment and Monitoring: Evaluate existing fraud controls, monitor fraud trends, and quantify exposure to evolving threats across digital and traditional channels.
  • Communication and Collaboration: Effectively present analytical findings and model performance to both technical and non-technical stakeholders, including fraud operations, compliance, and executive leadership.
  • Documentation and Reporting: Maintain comprehensive documentation of model development processes, risk assumptions, fraud detection frameworks, and compliance requirements.

Required Skills and Qualifications:

  • Education: Bachelor's or Master's degree in a quantitative discipline such as Statistics, Mathematics, Computer Science, Economics, Data Science, or a related field.
  • Experience: 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.
  • Skills: 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.

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