Fraud Analytics Lead — ML-Driven Detection & Strategy

GCash; Metro Manila; On-site; PHP 1,000,000 - 1,800,000; Full time

Job summary

GCash in the Philippines is seeking a data-driven fraud analytics leader to design and optimize detection rules and ML models to combat identity theft, ATO, and payment fraud. You will collaborate with Product, Engineering, and Customer Support to integrate fraud controls into the user journey.

You will analyze large datasets to detect fraud patterns, monitor KPIs like FPR and fraud rates, and lead incidents while ensuring robust documentation and approvals throughout the workflow.

Qualifications

Responsibilities

• Strategy & Implementation: Design, implement, and optimize and prioritize fraud detection rules and machine learning models to combat identity theft, account takeover (ATO), and payment fraud.
• Data Exploration: Analyze large datasets to identify emerging fraud patterns and "low-and-slow" attacks that traditional systems might miss.
• Performance Monitoring: Define and track KPIs such as False Positive Rates (FPR), Fraud Rates, and Operational Impact.
• Cross-Functional Leadership: Partner with Product, Engineering, and Customer Support to integrate fraud friction point into the user journey seamlessly.
• Tool Management: Oversee the selection and integration of third-party fraud prevention vendors and internal link-analysis tools.
• Incident Response: Lead analytical deep-dives during major fraud events to identify root cause and patch vulnerabilities immediately.
• Quality Assurance: Lead QA of fraud strategies developed by analysts with thorough documentation including business justification and expected impact.

Job description

GCash in the Philippines is seeking a data-driven fraud analytics leader to design and optimize detection rules and ML models to combat identity theft, ATO, and payment fraud. You will collaborate with Product, Engineering, and Customer Support to integrate fraud controls into the user journey.

You will analyze large datasets to detect fraud patterns, monitor KPIs like FPR and fraud rates, and lead incidents while ensuring robust documentation and approvals throughout the workflow.