- Community Home
- >
- Solutions
- >
- Tech Insights
- >
- Achieve banking insights on demand with AI
Categories
Company
Local Language
Forums
Discussions
Forums
- Data Protection and Retention
- Entry Storage Systems
- Legacy
- Midrange and Enterprise Storage
- Storage Networking
- HPE Nimble Storage
Discussions
Discussions
Discussions
Forums
Discussions
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
- BladeSystem Infrastructure and Application Solutions
- Appliance Servers
- Alpha Servers
- BackOffice Products
- Internet Products
- HPE 9000 and HPE e3000 Servers
- Networking
- Netservers
- Secure OS Software for Linux
- Server Management (Insight Manager 7)
- Windows Server 2003
- Operating System - Tru64 Unix
- ProLiant Deployment and Provisioning
- Linux-Based Community / Regional
- Microsoft System Center Integration
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Community
Resources
Forums
Blogs
- Subscribe to RSS Feed
- Mark as New
- Mark as Read
- Bookmark
- Receive email notifications
- Printer Friendly Page
- Report Inappropriate Content
Achieve banking insights on demand with AI
Artificial intelligence (AI) is the foundation for faster and more informed banking, enabling institutions to optimize, accelerate, and secure their operations. Here two experts in the financial services industry, Jennifer Smith, global marketing lead at HPE, and Kevin Levitt, global business development lead at NVIDIA, discuss what makes this achievable.
The banking industry is changing rapidly as institutions aim to be more effective and intelligent. Trends like rising cybersecurity risks, mobile banking, and digital payments combined with exponential data growth are challenging bank data management and performance. With so much intelligence to utilize and secure, institutions must find ways to quickly convert their data into insights, all at unprecedented speed and scale.
Data scientists are feeling the strain of these trends as they gather and interpret large sets of structured and unstructured data types. Without the right resources, data scientists have to work harder to collect, analyze, and model data—from diverse sources like transactions, customer information, customer engagements, daily operations, and workflows—then turn their findings into actionable plans. Banks require these critical inputs to develop best practices, from value and risk modeling to predictive analytics.
A survey of 1,440 banks found that fast-growing institutions are more likely to invest in technology that supports data initiatives versus other types of technology. These institutions make ongoing investments to expand data usage and drive business growth. Still, many banks need the infrastructure to manage the flood of information. Legacy environments lack the compute, speed, and flexibility to execute intensive data and analytics workloads. To better utilize their data, banks are searching for next-generation solutions to help them efficiently allocate resources, improve performance, and make smarter decisions.
Gaining insight on demand with AI
Banks are leveraging artificial intelligence (AI) capabilities to transform their operations. AI is the foundation for faster and more informed banking, enabling institutions to optimize, accelerate, and secure their operations. Banks that invest in AI technologies are accelerating time-to-value in four main applications—fraud detection and identity verification, conversational AI and speech recognition, robotics process automation (RPA) for document processing, and recommendation engines.
Risk management is a chief concern in banking—an initiative spanning security, regulatory compliance, fraud, anti-money laundering (AML), and know your customer (KYC) guidelines. With cyberattacks and other criminal activity on the rise, fraud detection and identity verification are crucial to mitigate as well as prevent breaches. These applications perform real-time calculations of risk and fraudulent exposure by processing countless transactions, payments, and customer details. Banks can flag anomalies in milliseconds and take immediate action to solve problems and heighten data security.
Conversational AI and automatic speech recognition use natural language inputs to enhance customer support experiences. Instead of taking call notes and feeding information into the system, banks transcribe events in the call center in real-time. The applications analyze video and audio content instantly to gain insights from customer engagements and streamline daily processes.
Banks employ RPA for document processing to extract meaning from unstructured document types. This allows banks to automate workflows, using AI to interpret documents and mimic human-like decisions, eliminating the time and costs of processing events manually.
Recommendation engines are designed to further improve customer experiences. Using AI and machine learning models, recommenders leverage troves of data—such as credit scores, outstanding balances, account utilization, or personal detail—to offer personalized suggestions and assess risk. AI enables recommendation engines to process larger models and more data at once.
Despite these benefits, many banks struggle to deploy and scale AI. To overcome these obstacles, banks must create a technology environment that can support a new breed of intelligent applications.
Fueling AI with breakthrough solutions
HPE is collaborating with NVIDIA to empower banking insight on demand from edge to cloud. The HPE AI platform powered by NVIDIA features cutting-edge solutions that are designed to enable faster, smarter, safer banking.
The HPE Apollo 6500 Gen10 Plus Systems and HPE ProLiant DL380 Gen10 Servers are NVIDIA-Certified Systems, validated for the performance, manageability, security, and scalability required for modern applications. The HPE AI platform also features Cray ClusterStor E1000 Storage System, delivering high throughput to GPUs with unprecedented agility to execute demanding AI workloads.
These systems make it simple to build and deploy intelligent applications to identify critical insights in vast amounts of data, calculate risk, and automate routine tasks. Banks can configure software to fit their specific requirements, so data scientists can focus on their AI objectives. Supported by a robust ISV banking ecosystem, GPU-optimized software, lets data scientists, developers, and researchers get back to building solutions, gathering insights, and speeding time-to-value.
Banks can choose to deploy solutions on-premises—in conjunction with virtual desktop infrastructure (VDI)—or as a service. Beyond traditional financing and leasing, HPE Financial Services and HPE GreenLake offer AI as a service, a pay-per-use consumption model with the security and control of on-premises IT. Cloud services help you innovate faster by making your applications and data accessible from anywhere. HPE GreenLake for VDI furthers these capabilities with VDI as a service, which is easily scalable and eliminates the need for costly upfront investments.
HPE Pointnext Services are available to help you plan and execute your AI roadmap. We discuss your banking goals, data requirements, and challenges to create a solution that is future proof for today’s and tomorrow’s applications.
Getting the most from your banking data
Solutions from HPE in partnership with NVIDIA provide exceptional intelligence and performance, all while driving down your TCO. Whether you are building an AI strategy or deploying multiple applications, the HPE AI platform is built to harness the full power of your data.
Let us help you unlock greater banking insight through the value of AI.
Meet our Tech Insights bloggers
Jennifer Smith leads Global Marketing at HPE for the financial services industry (FSI). She is strategically delivering innovative solutions in areas such as Hybrid Cloud, AI, Data Analytics and Virtualization to help FSI customers achieve real business outcomes. Jennifer received her B.A. in Industrial Engineering from Georgia Institute of Technology and MBA from Owen Graduate School of Management at Vanderbilt.
Kevin Levitt leads global business development for the financial services industry at NVIDIA. He focuses on global trends in accelerated compute and AI across financial services, including fintech, retail banking, credit card, and insurance. Kevin holds a B.A. from American University in Washington and an M.B.A. from the R.H. Smith School of Business at the University of Maryland.
Insights Experts
Hewlett Packard Enterprise
twitter.com/HPE_AI
linkedin.com/showcase/hpe-ai/
hpe.com/us/en/solutions/artificial-intelligence.html
- Back to Blog
- Newer Article
- Older Article
- Amy Saunders on: Smart buildings and the future of automation
- Sandeep Pendharkar on: From rainbows and unicorns to real recognition of ...
- Anni1 on: Modern use cases for video analytics
- Terry Hughes on: CuBE Packaging improves manufacturing productivity...
- Sarah Leslie on: IoT in The Post-Digital Era is Upon Us — Are You R...
- Marty Poniatowski on: Seamlessly scaling HPC and AI initiatives with HPE...
- Sabine Sauter on: 2018 AI review: A year of innovation
- Innovation Champ on: How the Internet of Things Is Cultivating a New Vi...
- Bestvela on: Unleash the power of the cloud, right at your edge...
- Balconycrops on: HPE at Mobile World Congress: Creating a better fu...
-
5G
2 -
Artificial Intelligence
101 -
business continuity
1 -
climate change
1 -
cyber resilience
1 -
cyberresilience
1 -
cybersecurity
1 -
Edge and IoT
97 -
HPE GreenLake
1 -
resilience
1 -
security
1 -
Telco
108