Today, AI-powered banks see advantages in applying the technology to a gamut of mission-critical needs—from customer service and fraud prevention to meeting environmental, social and governance standards. With AI to enhance every line of business and function, banks report significant return on investment (ROI) including the ability to increase productivity, reduce risk and keep customers happy. Globally, financial institutions are leaning into AI technologies, noting the potential to deliver up to $1 trillion of added value each year.1
It’s clear that AI is transforming the way banks operate, but what’s not always clear is how they can successfully implement and deploy AI projects. Experts recommend a shared, centralized infrastructure for AI—a full-stack solution that includes both hardware and software. This approach, known as AI as a platform, is ideal for three reasons. One, it consolidates expertise, productivity, and scale. Two, it shortens the lifecycle from development to deployment. And three, it drives down total cost of ownership with an efficient utilization of compute and storage resources.
Advantages of the AI-Powered Bank
As banks sift through volumes of data, AI can help them quickly discover patterns and identify key insights, calculate risk and automate routine tasks. Work performed by AI is done at extraordinary speed and scale with the added benefits of the technology being adaptable and essentially tireless. Executives have taken notice. While 89% of Directors say digital is embedded in all business growth strategies, they identify AI as the top breakthrough technology.2 Here we explore the most advantageous and strategic business use cases of AI for banking.
Anti-money laundering and identity verification: In a world where many people do their banking online, a well-planned transaction monitoring system has proven fundamental for an effective anti-money laundering (AML) system. AI is helping many banks boost accuracy for their AML and their Know Your Customer (KYC) system as part of identity verification.
Traditional rule- and scenario-based approaches to fighting financial crimes have made money laundering an ongoing challenge for compliance, monitoring and risk organizations. Rules often fail to capture the latest trends in money-laundering behavior. Alternatively, AI machine learning (ML) models can build sophisticated algorithms using granular, behavior-indicative data. These models are also more flexible, able to quickly adjust to trends and improve over time.
Even minor improvements in detection accuracy can significantly lower costs and improve regulatory compliance. Banks have been able to reduce false positives in transactional fraud detection using AI capabilities such as deep learning, computer vision and natural language processing. AI has also helped enhance identity verification in compliance with AML and KYC requirements.
Improving transactional fraud prevention: Fraud detection can be complicated as perpetrators continuously update their schemes. Online fraud losses are expected to reach $48 billion annually this year,3 making fraud detection and prevention a top use case for AI.
Because AI/ML can process massive amounts of data in milliseconds, the technology is able to understand and apply rules of fraud detection, increasing accuracy. The speed of AI attracts many major banks as it can fight fraud while protecting the customer experience by not introducing delays in processing credit card transactions.
One leading global financial institution uses a fraud-detection AI/ML system that employs supervised learning to look for established fraud patterns and unsupervised learning to identify emerging fraud patterns in real time. With every transaction, the algorithms examine, for example, a cardholder’s buying habits, geographic location, travel patterns, and real-time card usage data. The result is a more trustworthy transaction experience for legitimate cardholders and merchants with real-time barriers to stop criminals.
Virtual assistants and chatbots: Customer experience may be more important than ever. With its ability to resolve customer queries while reducing operational cost, conversational AI rules the day. In fact, a new study anticipates automation will save banks $7.3 billion globally this year—that’s 862 million hours, equivalent to nearly half a million working years.4
Guided by natural language processing, AI-powered automated systems can deliver highly personalized experiences to answer a variety of customer service requests. Chatbots and virtual assistants are able to open new accounts, field questions about existing accounts, assist with investments and trades, report lost or stolen cards, as well as help with fraud detection. One day the work may even be done by digital avatars, creating an omnichannel experience for bank customers.
Selecting the Right AI Solutions
Once a privilege of only the largest financial institutions, AI is now widely available for banks of all sizes to design, deploy and build solutions safely, quickly and cost effectively. There’s just one catch. To get the most from its AI investment in terms of performance and scalability, a business will need a reliable infrastructure made up of HPC, storage and networking.
Many banks are partnering with Dell Technologies for an AI-as-a-platform approach. By applying a portfolio of Dell Validated Designs for AI, organizations have experienced a 20% faster time to value and benefits of $55.76 million over three years.5 Offerings from Dell Technologies, featuring Intel® Xeon® processors, include servers, storage, networking, software and services proven in labs and in customer deployments.
The latest Intel® Xeon® processors have built-in accelerators which improve performance across AI, data analytics, storage and HPC workloads. This includes accelerated AI inferencing – up to 10x higher PyTorch real-time inference performance with built-in Intel® Advanced Matrix Extensions (Intel® AMX) (BF16) vs. the prior generation (FP32).6 HPC built-in acceleration provides increased performance for financial services—up to 45% higher average FSI performance vs. the prior generation.7
As one CTO said, “The hardware would usually be the problem, but our partnership with Dell Technologies has taken that off the table. With Validated Designs for AI, instead of focusing on the setup, we can focus on developing our AI solution and delivering business value.”
Beyond the numbers, banks are seeing unquantified benefits including increased employee satisfaction, greater success recruiting and retaining data scientists, improved customer reputation and environmental impacts. Case in point: An AI-driven reduction in fraud keeps more data secure, protecting customer livelihoods and keeping employee productivity high while safeguarding the organization’s financial health.
As AI-powered banks make major strides, they must do so at scale while meeting compliance and regulatory requirements—and most importantly, keeping customers satisfied. Hence the importance of selecting the right technology foundation. Backed by a solid AI deployment, financial institutions will see major gains in business intelligence, productivity and ROI.
 See [A17] at intel.com/processorclaims: 4th Gen Intel® Xeon® Scalable processors. Results may vary.
 See [H1] at intel.com/processorclaims: 4th Gen Intel® Xeon® Scalable processors. Results may vary.