Monroe Consulting Indonesia is partnering with a rapidly growing digital bank to hire a Product Manager (Fraud, Trust & Risk). This role sits at the intersection of Product, Risk, Fraud, and Data, focused on strengthening security controls while maintaining a seamless customer experience.
Key Responsibilities
- Own and drive the product roadmap across fraud detection, identity verification, KYC/AML, and transaction monitoring.
- Design risk-based product journeys that minimize customer friction while preventing financial crime.
- Translate fraud rules and risk policies into scalable product and system requirements.
- Monitor emerging fraud trends and continuously enhance platform defenses.
- Lead Trust & Risk initiatives across Risk, Compliance, Legal, Data, and Engineering teams.
- Partner with Data teams to deploy fraud and risk scoring models into production.
- Ensure compliance with Indonesian financial regulations and internal standards.
- Define success metrics (fraud rate, false positives, KYC pass rate, customer friction) and drive data-led improvements.
- Manage end-to-end product delivery including research, documentation (PRDs, user stories), agile execution, and stakeholder communication.
- Conduct root cause analysis on fraud incidents and implement continuous product enhancements.
- Benchmark capabilities against industry best practices and run experiments/A-B tests before full rollout.
Qualifications
- 6+ years of Product Management experience, ideally in fintech, digital banking, lending, or payments.
- Hands-on exposure to fraud prevention, risk systems, and financial crime controls.
- Strong understanding of KYC/AML, identity verification, and transaction monitoring frameworks.
- Data-driven mindset with the ability to collaborate closely with Data Science teams.
- Strong analytical thinking and structured problem-solving skills.
- Excellent communication skills in English and Indonesian.
- Experience in digital banking or P2P lending
- Familiarity with third-party fraud tools
- Understanding of ML-based risk models
- Relevant risk or financial crime certifications.
