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 Learns™ 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.