Principal Fraud Analyst

At NOVACARD , we’re redefining how people use credit. We are the first interest-free and no-annual-fee credit card in Mexico, designed to simplify personal finances and give users complete control - all from a mobile app. With NOVACARD, users can access up to $200,000 MXN in credit, only pay when they use it, and manage everything digitally in under 5 minutes. Our mission is to empower people to make smarter financial decisions by offering flexibility, transparency, and the freedom they need to reach their goals. Simple finances, big goals.

About the Role:
We’re looking for a Principal Fraud Analyst to join the NOVACARD Risk Tech team and lead the development of fraud prevention strategies across our lending products. In this role, you will design and implement fraud detection frameworks for POS lending, cash loans, and card products, analyze emerging fraud patterns, and develop detection rules while collaborating with Data Science, Risk, and Product teams to strengthen fraud controls, improve monitoring, and maintain a strong balance between fraud prevention and approval rates across global markets.

Key Responsibilities:

• Developing a partner onboarding and KYB framework: Design and implement a methodology for merchant and agent verification during onboarding. Establish admission criteria, partner reliability scoring models, and automated checks to detect and prevent potential fraud schemes at an early stage.

• Building end-to-end fraud prevention for POS lending: Design and launch a comprehensive fraud prevention framework for POS business — from application submission at the point of sale to behavioral monitoring throughout the entire credit lifecycle.

• Merchant & Agent fraud detection: Develop and implement monitoring systems to detect suspicious activity among merchants and agents. Identify collusion schemes, data manipulation, and organized fraud operations at points of sale.

• Cross-product fraud rule architecture: Design and maintain fraud detection logic across approval processes and transaction monitoring for the full product portfolio (POS, Cash Loans, Cards).

• Data effectiveness management: Conduct in-depth analytics and integrate external data sources, evaluating their impact on key metrics such as Approval Rate and Fraud Rate.

• R&D and funnel optimization: Continuously improve the balance between risk control and system throughput, focusing on reducing false positives while maintaining effective fraud protection.

• Expert audit and QA: Perform selective quality reviews of investigator cases, calibrate investigation processes, and provide expert support on complex or non-standard fraud cases.

• Feature engineering collaboration: Generate hypotheses and prepare requirements for the Data Science team to develop new fraud detection features based on anomalies identified in POS and card transactions.

• Development of the analytical environment: Support data marts in the DWH and contribute to the development of analytical reporting.