Chief Fraud Risk Analyst

3 days ago

Anantapur, Andhra Pradesh, India beBeeFraudRisk Full time ₹ 50,00,000 - ₹ 1,00,00,000
Senior Fraud Risk Modeler Job Description

We are seeking a highly skilled Senior Fraud Risk Modeler to join our team. This role is responsible for designing, developing, and maintaining models to detect and prevent fraud across various financial products and channels.

The ideal candidate will have 5+ years of experience in fraud analytics and modeling, with strong proficiency in Python, SQL, and data science tools. They 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.

  • Model Development & Validation: Develop and validate fraud detection and prevention models using advanced statistical and machine learning techniques.
  • 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.
  • Communication & Collaboration: Effectively present analytical findings and model performance to both technical and non-technical stakeholders.
  • Documentation & Reporting: Maintain comprehensive documentation of model development processes, risk assumptions, fraud detection frameworks, and compliance requirements.
Requirements:

To be successful in this role, you should have the following 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.

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