About the Role
We are looking for a hands-on Payment Risk Architect to build and scale our end-to-end payment risk and fraud decisioning capabilities for a global digital product.
This role will own the architecture and implementation of our risk decisioning framework, working closely with data, payments, and product teams to identify bottlenecks, improve payment success rates, and control fraud risk.
Key Responsibilities
1. Build Risk Decisioning System (0 → 1)
- Design and implement a risk decisioning framework, including rule engine, risk scoring logic, and decision flows (approve / challenge / block)
- Enable real-time transaction evaluation and scalable architecture
2. Optimize Payment & Risk Performance
- Analyze the full payment funnel: attempt → authentication → success → failure → drop-off
- Identify bottlenecks across countries, payment methods, and user segments
- Balance fraud prevention, approval rate, and user experience
3. Define Risk Signals & Data Layer
- Design and integrate key signals such as device fingerprinting, IP/geolocation intelligence, behavioral patterns, and velocity checks
- Work with data and engineering teams to enable real-time and historical risk analysis
4. Integrate with Payment Ecosystem
- Collaborate with payment providers (acquirers, gateways, APMs)
- Analyze and optimize decline reasons, authentication flows (e.g. 3DS), and payment routing
- Improve payment acceptance rates across regions
5. Drive Execution (Hands-on)
- Translate strategy into deployable rules and systems
- Work closely with engineering teams to implement solutions
- Iterate quickly based on data and outcomes
6. Fraud Monitoring & Continuous Optimization
- Monitor and improve fraud rate, chargeback rate, and false positive rate
- Continuously refine risk decisioning logic
Requirements
Must-have
- 5+ years of experience in payments, fraud, risk, or fintech
- Experience designing or building risk engines, fraud detection systems, or decisioning workflows
- Strong understanding of card payments (authorization, issuer behavior, 3DS)
- Familiar with payment flows, decline scenarios, and transaction lifecycle
- Hands-on experience with data analysis (SQL required, Python is a plus)
- Ability to translate business problems into technical solutions
Preferred
- Experience in payment companies such as Stripe, Adyen, Grab, or similar
- Experience with real-time systems and scalable architectures
- Familiarity with fraud scoring, risk models, or machine learning approaches
- Exposure to global payment methods (APMs, wallets, cross-border payments)
Success Metrics
- Improvement in payment success rate
- Reduction in fraud and chargeback rates
- Reduction in false positives
- Faster detection and response to risk events
