Why financial institutions are using AI

AI For Business

Today, AI-powered banks are finding benefits in applying technology to a variety of mission-critical needs, from customer service and fraud prevention to meeting environmental, social, and governance standards. By using AI to power all business units and functions, banks are reporting significant return on investment (ROI), including increased productivity, reduced risk, and maintained customer satisfaction. Globally, financial institutions are turning to AI technology, eyeing the potential to add up to $1 trillion in added value each year.1

While it is clear that AI is transforming how banks operate, it is not always clear how AI projects can be implemented and deployed. 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 is to combine expertise, productivity and scale. The second is to shorten the development-to-deployment lifecycle. And third, lower total cost of ownership through efficient use of compute and storage resources.

Advantages of AI-powered banks

As banks sift through massive amounts of data, AI can help them quickly spot patterns and identify key insights, calculate risk, and automate routine tasks. The work performed by AI is done at extraordinary speed and scale, with the added advantage of adaptive and inherently tireless technology. Executives took notice. His 89% of directors say digital is part of all business growth strategies, but recognizes AI as the most disruptive technology.2 Here we explore the most advantageous and strategic business use cases for AI for banking.

Anti-money laundering and identity verification: In a world where many people bank online, a well-planned transaction monitoring system has proven to be the cornerstone of an effective anti-money laundering (AML) system. AI is helping many banks improve the accuracy of their AML and Know Your Customer (KYC) systems as part of identity verification.

Traditional rules- and scenario-based approaches to combating financial crime make money laundering an ongoing challenge for compliance, surveillance, and risk organizations. Rules often fail to capture the latest trends in money laundering practices. Alternatively, AI machine learning (ML) models can use granular behavioral data to build sophisticated algorithms. These models are also highly flexible, allowing you to adapt quickly to trends and improve over time.

Even small improvements in detection accuracy can significantly reduce costs and improve regulatory compliance. Banks have been able to reduce false positives in transaction fraud detection using AI capabilities such as deep learning, computer vision, and natural language processing. AI has also helped strengthen identity verification in compliance with AML and KYC requirements.

Improved transaction fraud protection: Fraud detection can be complicated as perpetrators continually update their schemes. Online fraud losses are expected to reach $48 billion annually this year.3 Fraud detection and prevention is the number one use case for AI.

Because AI/ML can process large amounts of data in milliseconds, the technology can understand and apply fraud detection rules to improve accuracy. The speed of AI has attracted many large banks as it can combat fraud while protecting the customer experience by introducing no delays in processing credit card transactions.

A leading global financial institution uses a fraud detection AI/ML system that uses supervised learning to look for established fraud patterns and unsupervised learning to identify emerging fraud patterns in real time. For each transaction, the algorithm looks at the cardholder’s buying habits, geographic location, travel patterns, real-time card usage data, and more. The result is a more trustworthy transaction experience for legitimate cardholders and merchants with real-time barriers to deter criminals.

Virtual assistants and chatbots: Customer experience may be more important than ever. Conversational AI dominates everyday life with the ability to solve customer queries while reducing operational costs. In fact, automation could save the bank $7.3 billion globally this year, according to a new study. This equates to 862 million hours for him, or nearly 500,000 years of work.Four

AI-powered automated systems can deliver highly personalized experiences guided by natural language processing to answer a variety of customer service requests. Chatbots and virtual assistants can open new accounts, ask questions about existing accounts, assist with investments and transactions, report lost or stolen cards, and help detect fraud. . Someday, the work may be done by digital avatars that provide an omnichannel experience for bank customers.

Choosing the Right AI Solution

Once the prerogative of only the largest financial institutions, AI is now widely available to banks of all sizes to design, deploy and build solutions securely, quickly and cost-effectively. I can. Only one catch. Getting the most out of your AI investment in terms of performance and scalability requires a reliable infrastructure consisting of HPC, storage, and networking.

Many banks are partnering with Dell Technologies for their AI-as-a-platform approach. By applying the Dell Validated Designs for AI portfolio, the organization has seen a 20% reduction in time to value, earning him $55.76 million over three years.Five Dell Technologies products powered by Intel® Xeon® processors include servers, storage, networking, software, and services proven in labs and customer deployments.

The latest Intel® Xeon® processors have built-in accelerators that improve performance across AI, data analytics, storage, and HPC workloads. This includes accelerated AI inference. Up to 10x higher PyTorch real-time inference performance compared to previous generation (FP32) with built-in Intel® Advanced Matrix Extensions (Intel® AMX) (BF16).6 Built-in HPC acceleration improves financial services performance. Up to 45% better average FSI performance compared to the previous generation.7

As one CTO said: With Validated Designs for AI, you can focus on developing AI solutions and delivering business value, instead of focusing on setup. ”

Beyond the numbers, banks are realizing unquantified benefits such as improved employee satisfaction, successful recruitment and retention of data scientists, improved customer reputation, and environmental impact. Case in point: AI-driven fraud reduction keeps more data safe, protects the lives of customers, keeps employees productive, and protects an organization’s financial health.

As AI-powered banks make great strides, they must do so while meeting compliance and regulatory requirements, and most importantly, keeping customers happy. Therefore, choosing the right technology foundation is critical. Backed by solid AI deployments, financial institutions are realizing significant gains in business intelligence, productivity, and ROI.

See what Dell can do for financial institutions.


[1] https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-executives-ai-playbook?page=industries/banking/

[2] https://www.gartner.com/en/newsroom/press-releases/2022-10-19-gartner-says-89-percent-of-board-directors-say-digital-is-embedded-in-all- business growth strategy

[3] https://www.insiderintelligence.com/insights/ai-in-finance/

[4] https://www.juniperresearch.com/press/bank-cost-savings-via-chatbots-reach-7-3bn-2023

[5] https://www.delltechnologies.com/asset/en-us/products/ready-solutions/industry-market/forrester-tei-dell-ai-solutions.pdf

[6] look [A17] intel.com/processorclaims: 4th Generation Intel® Xeon® Scalable Processors. Your results may vary.

[7] look [H1] intel.com/processorclaims: 4th Generation Intel® Xeon® Scalable Processors. Your results may vary.

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