According to experts at Forbes, big changes are coming to the banking industry over the next five years. A few of these include: the expansion of digital offerings and digital currencies, closure of brick-and-mortar branches, commitment to sustainability, focus on security solutions, and a move towards tech-driven simplification.
To modernize and thrive in the face of these trends, banks must tap into the vast amounts of data generated everywhere – from the data center to remote edge devices. They must be able to gain critical insights, calculate risk, and automate routine tasks – at unprecedented speed and scale. Effectively doing so will help banks innovate, grow, and be poised for future success. The inability to do so can lead to missed business opportunities and lost profits.
Artificial intelligence (AI) and analytics are a top priority for banks
Fortunately, advancements in artificial intelligence (AI) are enabling banks to drive efficiencies across their operations, innovate faster, and personalize the customer experience. Specifically, AI technologies benefit banks in four critical areas: fraud detection and identity verification, conversational AI, robotics processing automation (RPA), and recommendation engines. It should come as no surprise that banking is one of the top three industries making the largest annual investments in big data and analytics.
AI is easier said than done
While AI, machine learning (ML), and analytics are top priorities for banking institutions, the implementation and deployment of these technologies are slowed by numerous bottlenecks. According to industry analysts, a large majority of enterprises struggle with the “last mile” of their AI deployment and management due to business and IT integration challenges. Some of the most common challenges banks need to overcome are:
- Long IT provisioning time for new AI/analytics environments
- Managing impending growth that will be a challenge for the existing IT environment
- Inefficient infrastructure utilization on premises resulting in high cost per workload
- Reducing the growing number of ML sandboxes to gain greater visibility, trackability, and auditability
- Legacy, monolithic data warehouses and lakes that have locked data in place
- Legacy apps that are too costly and time consuming to refactor or require the rewrite of code
HPE and NVIDIA introduce the AI platform for banking
In partnership with NVIDIA, HPE has introduced the AI platform for banking—an open, cloud-like platform, hybrid in design—to help customers deploy and manage effective AI infrastructure and overcome enterprise AI and ML operations challenges. The AI platform for banking solution is engineered to help banks gain faster and broader insights to rapidly innovate, operate more effectively, and realize better business outcomes—all while driving down the total cost of ownership (TCO).
With the AI platform for banking, line of business executive can collaborate on requirements, data scientists and developers can build AI algorithms and applications faster, DevOps can deploy their applications reliably, and IT can support the entire process by deploying the ideal hardware configurations needed for the workloads. With a robust software layer and infrastructure that is flexible, scalable, and supports applications with heterogenous workloads and architectures, banks can deliver AI applications that tackle the four critical areas mentioned above. Read page 4 of the solution overview to see how.
Key components of the AI platform for banking
The AI platform for banking creates one tightly integrated solution to help banks deploy enterprise AI and keep pace with evolving industry trends and regulations. It is comprised of the following elements:
HPE Ezmeral Software
- HPE Ezmeral ML Ops
- HPE Ezmeral Runtime Enterprise
- HPE Ezmeral Data Fabric
- HPE Ezmeral Unified Analytics
HPE Reference Architecture for AI-Optimized Infrastructure
- Compute: HPE Apollo 6500 Gen10 Plus with NVIDIA A100 GPUs
- Networking: Compute/storage fabric, in-band management network with NVIDIA Quantum InfiniBand, and out-of-band management network
- Storage: Cray ClusterStor, Parallel File System
Professional Services and Expertise
(Read page 5 of the solution overview for a deeper dive into each element.)
“HPE has the orchestration platform, data processing, data lake, and data visualization capabilities, but the nuance piece is where NVIDIA comes in. NVIDIA helps us do the work we are trying to accomplish with building intelligent data pipelines for better business outcomes and better insights with AI and ML. Tying into the NVIDIA stack, whether it’s through NGC or Rapids, allows for enhanced performance analytics and provides opportunity for customers to get the most from their Apache Spark solutions.” –Matthew Morris, Technical Marketing Engineer, HPE & presenter at GTC 2021
Finally, if you are ready to jumpstart your AI deployment or need guidance with your AI strategy, download the HPE-NVIDIA solution overview: Unleashing the future of banking with AI: Empower key applications with the AI platform for banking and contact us today.
Lola
Hewlett Packard Enterprise
HPE Ezmeral on LinkedIn | @HPE_Ezmeral on Twitter
@HPE_DevCom on Twitter
LolaTam
Lola Tam is a senior product marketing manager, focused on content creation to support go-to-market efforts for the HPE Enterprise Software Business Unit. Areas of interest include application modernization, AI / ML, and data science, and the benefits these solutions bring to customers.
