Sr Fraud Risk Modeler

Job Title: Senior Fraud Risk Modeler (Credit Card)

About Straive:

Straive is a market leading Content and Data Technology company providing data services, subject matter expertise, & technology solutions to multiple domains. Data Analytics & Al Solutions, Data Al Powered Operations and Education & Learning form the core pillars of the companys long-term vision. The company is a specialized solutions provider to business information providers in finance, insurance, legal, real estate, life sciences and logistics. Straive continues to be the leading content services provider to research and education publishers. Data Analytics & Al Services: Our Data Solutions business has become critical to our client's success. We use technology and Al with human experts-in loop to create data assets that our clients use to power their data products and their end customers' workflows. As our clients expect us to become their future fit Analytics and Al partner, they look to us for help in building data analytics and Al enterprise capabilities for them. With a client-base scoping 30 countries worldwide, Straives multi-geographical resource pool is strategically located in eight countries - India, Philippines, USA, Nicaragua, Vietnam, United Kingdom, and the company headquarters in Singapore.

Website:

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. Youll 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 Masters. 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.