In-Person: Streaming meetup Apache Kafka® for Fraud Detection & Infinite Kafka

Join us on June 11th from 6:00pm for a Data Streaming meetup hosted by Moniepoint!

PLEASE bring your PHOTO ID and REGISTER with your First and Last Name. For security purpose

🗓 Agenda:

• 6:00pm – 6:30pm: Food/Drinks and Networking
• 6:30pm - 7:15pm: Tom Scott, CEO, Streambased
• 7:15pm - 8:00pm: Abraham Imohiosen, Engineering Manage, Moniepoint
• 8:00pm - 8:30pm: Q&A Networking.

💡Speaker One: Tom Scott, CEO, Streambased

Title of Talk: Infinite Kafka? Rethinking Retention with Iceberg

Abstract: Apache Kafka is designed for high-throughput, low-latency event streaming, not cost-efficient long-term storage. Yet we constantly see cases like event sourcing, audit/compliance, and large-scale reprocessing forcing that pattern onto it.

As retention increases, costs grow linearly to support edge cases, one-time runs, and “checkbox” use cases.

Can Iceberg help here?

In this talk, Tom explores a hybrid architecture that separates hot and cold data while preserving Kafka’s log semantics. Using a combination of Kafka and Apache Iceberg, he demonstrates how to extend Kafka into low-cost object storage, enabling effectively unlimited retention without sacrificing performance or access patterns.
The result is a unified log that supports both real-time processing and long-term replay, removing the traditional trade-off between cost and capability in Kafka-based systems.

Bio: Long-time enthusiast of Kafka and all things data integration, Tom has more than 15 years of experience in innovative and efficient ways to store, query, and move data. Tom is currently CEO at Streambased, a company focused on unifying operational and analytical data estates into a single, consistent, and efficient data layer.

💡Speaker Two: Abraham Imohiosen, Engineering Manager, Fraud Prevention Tools.

Title of Talk: From CDC to Decision: Kafka as the Fraud Detection Pipeline's Connective Tissue

Abstract: Fraud detection isn't one system — it's a system of moving parts (databases, feature stores, rule engines, ML models, case management tools) that all need to agree on what just happened, in milliseconds. Kafka sits in the middle of it, and treating it as "just the message bus" leaves a lot of value on the table. In this talk, I'll walk through three jobs Kafka does inside Moniepoint's fraud detection pipeline: moving events between services, powering real-time aggregations and windowed features, and acting as a CDC source that turns database changes into the canonical stream feeding a final aggregate store, as well as routing evaluated events into the case management system for final decisioning.

Bio: Abraham Imohiosen is an Engineering Manager at Moniepoint, where he leads the Fraud Prevention team in building case management systems and machine-learning detection models that protect millions of customers and billions in transaction volume. He has over 10 years of experience across fintech and cloud-based architectures, having previously led the delivery of Monieworld Transfers and a savings product. Abraham holds an M.Sc. in Robotic Systems Engineering from RWTH Aachen University.

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If you are interested in hosting/speaking at a meetup, please email community@confluent.io