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Quick Demo: Kafka + Simple Fraud Model Streaming Pipeline
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Classroom Contents
The Role of Machine Learning in Today's Payment Ecosystems
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- 1 Welcome & What This Talk Covers ML in Payments, Not Algorithms
- 2 Speaker Intro: Real-World Payments Experience & Why ML Matters
- 3 Agenda Overview: Lifecycle Touchpoints, Benefits, Future & Risks
- 4 Modern Payment Ecosystem 101: Domains, Interdependencies & Latency Demands
- 5 Rules vs ML: From Static Thresholds to Real-Time, Personalized Decisions
- 6 Where ML Fits in the Payment Lifecycle Acquisition → Ops
- 7 Use Case #1: Merchant Acquisition—Automated Risk Scoring & Faster Onboarding
- 8 Use Case #2: Transaction Authorization—Real-Time Fraud Scoring & Smart Routing
- 9 Use Case #3: Operations—Predictive Monitoring, Auto-Scaling & Incident Response
- 10 System-Level Wins: Scalability, Automation & Sub-100ms Intelligence
- 11 ML as a Platform Layer: Data → Intelligence → Applications → Business Value
- 12 Challenges & Considerations: Data Quality, Explainability, Latency, Monitoring, Integration
- 13 The Future of ML in Payments: Embedded Finance, Personalization, Cross-Border & Autonomy
- 14 Key Takeaways: ML as the New Foundation for Payment Platforms
- 15 Quick Demo: Kafka + Simple Fraud Model Streaming Pipeline
- 16 Wrap-Up, Q&A, and How to Connect
