Feedzai Partners with Azul Systems: Zing to Enable Faster Processing of Massive Data Sets to Ensure Delivery of Fraud Analysis and Protection in Real-Time
Cassandra Summit 2014 and San Mateo/Sunnyvale, Calif. – September 10, 2014 – Feedzai, a data science company that uses real-time, machine-based learning to analyze big data to make commerce safe, and Azul Systems Inc. (Azul), the award- winning leader in Java runtime solutions, today announced a commercial partnership under which Feedzai is now an Azul Certified ISV partner. Through the partnership, Feedzai will use Zing®, Azul’s innovative Java Virtual Machine (JVM) as part of its Feedzai Fraud Prevention That Learn s™ software to provide ultra-fast processing of big data.
“The real-time analysis of data to prevent fraud in the financial industry is key to predicting and preventing fraud,” said Nuno Sebastiao, Chief Executive Officer of Feedzai. “It’s almost impossible to have ultra-low latencies—in the range of 5-10 milliseconds with a standard JVM—and our customers demand that. Azul powers the largest banks in the world and with peak load demands of up to 50,000 transactions per second, Zing will help ensure that we can deliver the best that artificially intelligent machines can offer.”
Zing is designed for enterprise applications and workloads that require any combination of large memory, high transaction rates, low latency, consistent response times and high sustained throughput. It is the only JVM that eliminates Java Garbage Collection (GC) pauses and is used by many of the world’s largest financial services institutions.
“We are happy to welcome Feedzai into our ISV partner program, and know that Zing will be a great fit for them as they continue to work with large and growing amounts of big data,” said Scott Sellers, CEO of Azul Systems. “With Zing, big data solutions can deliver consistent low-latency performance even with massive data sets, meeting the needs of ecommerce and a broad array of other payment solutions.”
Feedzai Fraud Prevention That Learns™ technology fuses big data and machine learning to allow analysts to predict and prevent electronic payment loss in real time based on behavioral analysis and understanding of the way consumers behave when they make purchases, online, in-store or from mobile devices. The software uses big data, including mobile and social data streams, to create deep learning profiles for each customer, merchant, location or POS device, product, with up to a three-year history of data behind it. This data helps acquirers, issuers and retailers mitigate risk, guard every transaction and preserve the customer experience.
Feedzai is a data science company that uses real-time, machine-based learning to help payment providers, banks and retailers prevent fraud in omnichannel commerce. Feedzai is a global company, with US headquarters in San Mateo and is backed by SAP Ventures, big data investment firm Data Collective, and other international investors. Companies that rely on Feedzai include Coca-Cola, Logica, Vodafone, Ericsson, SIBs Payment Solutions, Horizon Wind Energy, and Servebase Credit Card Solutions. For additional information, visit www.feedzai.com.
About Azul Systems
Azul Systems, the industry’s only company exclusively focused on Java and the Java Virtual Machine (JVM), builds fully supported, standards-compliant Java runtime solutions that help enable the real time business. Zing is a JVM designed for enterprise Java applications and workloads that require any combination of low latency, high transaction rates, large working memory, and/or consistent response times. Zulu is Azul’s freely available open source JVM based on OpenJDK, and Azul provides high-quality commercial support options with its Zulu Enterprise offering. For additional information, visit: www.azul.com.
Azul Systems, the Azul Systems logo, Zing, and Zulu are registered trademarks, and ReadyNow! is a trademarks of Azul Systems Inc. Java and OpenJDK are trademarks of Oracle Corporation and/or its affiliated companies in the United States and other countries. All other trademarks are the property of their respective holders.
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