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Payment fraud is rampant in the financial services industry. According to the American Bankers Association, fraud against bank deposit accounts totaled $25.1 billion in 2018.[1], In 2022, eight US senators sent letters to the CEOs of the seven largest US banks regarding a fraud at a real-time payments firm. Real-time payments to grow by 41% globally in 2020[2]There is a clear need to modernize fraud prevention as criminals seek to exploit the system.
To help combat payment fraud, companies are investing in technology that leverages hybrid cloud architecture and AI/ML. In hybrid cloud, compute workloads can be spread across on-premises data centers, private clouds, public clouds, and even edge locations based on requirements such as data sovereignty, latency, capacity, cost, and more. Advances in AI/ML allow machines to be trained to recognize patterns from billions or even trillions of data points. These relationships are then incorporated into “models” that are built into real-time payment workflows.
A hybrid architectural pattern is for high privacy paid infrastructure to remain on-premises with the public cloud being used for model training. Using the public cloud, firms can parallelize training across a large number of nodes, pay only for the time used, and gain access to hardware acceleration such as GPUs. To protect privacy or improve data quality, companies can generate synthetic data that is transferred to the cloud and used for training. Trained models are then imported into a firm’s runtime environment where they execute on-premises with local access to confidential data.
For global financial institutions, data sovereignty requirements may dictate another architectural pattern that keeps payment and fraud data in the country of origin. With federated learning, a single foundational model is built centrally and distributed to remote sites. These sites then train models on their own local, private data before sending their models back to the central site without privacy data. The models are then aggregated into a new global model that can be sent to remote sites for more iterative rounds of training. Once the model is fully trained, the model runs locally without transferring privacy data to any regulatory jurisdiction.
While architectures will differ based on needs, financial institutions will all agree that running these workloads at scale requires a modern platform that leverages the hybrid cloud, improving operational efficiencies , reduces operational risk and helps improve safety posture. With a platform like Red Hat OpenShift, companies can successfully build, modernize and deploy applications with a consistent experience both on-premises and in the cloud. As business needs evolve, workloads can be moved between on-premises servers or those running on Amazon AWS, IBM FS Cloud, Microsoft Azure, or Google Cloud. To learn more, visit red hat
– Eric Rosenbaum, Chief Technologist, Red Hat
Aric Rosenbaum serves as Chief Technologist on Red Hat’s Global FSI team, where he helps customers meet their strategic priorities through the use of open source technology. Prior to joining Red Hat, he led large, digital transformation projects in the Investment Management division of Goldman Sachs and was co-founder/CTO of several fintechs in equity and FX trading.
[1] American Bankers Association: 2019 Deposit Account Fraud Summary
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