Senior Fraud Risk Modeler

Job Title: Senior Fraud Risk Modeler (Credit Card)

Location: Bangalore(Preferred), Gurgaon

Experience: 6+ Years

Job Summary:

As a Senior Risk Modeler specializing in Fraud Risk, you will design, develop, and maintain models to detect and prevent fraud across various financial products and channels - primarily in the credit card domain. You'll 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.

• 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.

Qualifications:

• Bachelors or Master's. 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.