Low-latency computing and stream processing for financial systems (sponsored by Hazelcast)
In-Memory-Optimized Stream Processing and Machine Learning in Real-time Banking
Modern banking customers demand experiences that are real-time, global, and omnichannel. This creates tremendous technology challenges along with opportunities for disruption. It also brings many new vulnerabilities that require real-time machine learning.
In this talk, we will explore the challenges with integrating real-time machine learning into payment processing and securities trading applications.
We will walk through key technology areas needed to address these challenges, such as stream processing and low-latency compute, data locality and data-aware processing, and in-memory optimized solutions. We will discuss how these enable faster processing while also enabling greater resilience and scalability, and walk through real-world examples covering payment processing, real-time fraud, real-time pricing, and fast risk.
John DesJardins is currently VP Solution Architecture and CTO for North America at Hazelcast, where he champions growth and adoption of our in-memory computing platform. His expertise in large scale computing spans Big Data, Internet of Things, Machine Learning, Microservices, and Cloud.
John brings over 25 years of experience including 15 years of experience in architecting and implementing global scale computing solutions with leading financial services organizations at Hazelcast, Cloudera, Software AG and webMethods. He has helped architect and implement large scale applications with organization such as the European Central Bank, Morgan Stanley, JP Morgan Chase, CapitalOne, Standard Chartered, Visa, Discover, Paypal, Apple, AT&T, Comcast, Coca-Cola, and many others.
He holds a BS in Economics from George Mason University, where he first built predictive models, long before that was considered cool.
Prior to Hazelcast, he worked at Cloudera, Software AG and webMethods, helping Global 2K firms architect massively scalable solutions for ingest, analysis and storage of real-time data, as well as working on innovations in areas such as IoT and Machine Learning.
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