Although they often move slowly on account of their large size and heavy regulatory burdens, banks have frequently been leaders in the adoption of new and emerging technologies. For example, think about ATM networks, online banking, digital wallets and contactless payments, to name just a few of the game-changing innovations that banks have embraced over the years.
But what about artificial intelligence?
Like so many other technologies before it, the financial services industry and supporting software makers have leaned heavily into AI, according to Sean O’Dowd, global financial services strategy and solutions manager at Nutanix.
“When we look at financial services, be it banking, capital markets, insurance, everything is driven by information and data,” explained O’Dowd.
“The industry’s heavy reliance on data makes it ripe for AI because AI excels at making sense out of large swaths of digital information.”
Financial services companies embraced machine learning early as a way to accelerate high-frequency trading, and the uses of AI in financial services have only expanded since. From mitigating risks and improving customer service to generate business intelligence and more, here are just a few of the most exciting, bleeding-edge AI use cases coming out of the financial services sector today.
O’Dowd pointed to how ING worked with McKinsey to build, test, and launch a bespoke customer-facing chatbot that uses the latest gen AI technology to meet customer needs.
The global bank intends to extend the GenAI capabilities across 10 markets with the potential to impact more than 37 million customers across 40 countries.
“This project has helped establish a solid technical foundation that puts ING at the forefront of gen AI applications within the banking industry,” says Bahadir Yilmaz, ING Chief Analytics Officer in a digital report by McKinsey.
“Gen AI technology will further evolve and we feel well-positioned to leverage those developments in order to offer the customer the best experience and remain a technology leader in our industry.
O’Dowd also pointed to NatWest Retail Bank. They built Cora then Cora+, NatWest's next-generation assistant powered by Gen AI. After creating the bank's AI and data framework, covering key principles such as maintaining human oversight, removing bias, and considering socio-economic impacts, including how AI models consume energy, the bank’s digital team worked with IBM's Client Engineering team to create the virtual assistant powered by IBM watsonx Assistant and built on IBM Cloud. The multichannel agent provides natural answers to customers using data from multiple sources, including products, services, and banking information.
"We are finding out so much about how people and technology interact. Every day, Cora and Cora+ learn new things,” Wendy Redshaw, chief digital information officer at NatWest Retail Bank told ZDNet.
1. Stopping Scams
Consumers around the world lost more than $1 trillion to online scams last year, according to the Global Anti-Scam Alliance. A conversational AI chatbot, Daisy, is fighting back by tying up scammers’ time and frustrating their efforts.
Introduced by British mobile phone company O2, Daisy poses as an elderly scam victim, interrupting scammers’ work by making them think they’ve got a real victim on the hook.
“Using a custom large language model, Daisy can hold autonomous conversations with scam callers in real time,” CNN reported in November 2024. “While she does not intercept any calls, she has multiple phone numbers of her own that O2 has worked to get into circulation online.”
O’Dowd pointed to an article published by the Financial Review, where Maria Milosavljevic, chief information security officer at ANZ, explained how the bank uses AI in cybersecurity and analysing large amounts of data.
“The scale of what organisations have to do in cybersecurity has increased tremendously in recent years, and so perhaps the biggest potential for AI is to actually help organisations to stay on top of that,” she said.
2. Fraud Detection
Mastercard uses an AI-based system called Decision Intelligence to analyze transactions in real time for fraud detection.
“The algorithm uses ‘heat-sensing fraud patterns,’ assigning scores based on deviations from a cardholder’s typical behavior,” Chief Data Officer Magazine reported in February 2024. A recurrent neural network, developed in-house, powers the model.
Some 62 million Americans reported experiencing credit card fraud last year. “With generative AI we are transforming the speed and accuracy of our anti-fraud solutions, deflecting the efforts of criminals, and protecting banks and their customers,” Mastercard President of Cyber and Intelligence Ajay Bhalla told NASDAQ.com in March 2024.
Nutanix’s O’Dowd pointed to a collaboration between Santander UK and ThetaRay, a leading provider of AI-powered financial crime detection technology. The partnership was recognized for 'Best Use of Data for Human Trafficking and Modern Slavery Detection' at the 2024 Digital Transformation Awards, according to The International Business Times, which interviewed Peter Reynolds, CEO of ThetaRay. His company’s AI-driven solution is designed to analyze billions of transactions, establish a behavioral baseline for each entity, and detect subtle deviations from this norm. These deviations can be the key to identifying human trafficking.
3. Customer Service
Bank of America’s voice- and chat-activated AI assistant, Erica, helps customers navigate banking tasks like transferring money between accounts, identifying duplicate charges and paying bills.
Its adoption rate shows just how fast an AI-supported solution can gain traction: Erica took four years to engage in its first 1 billion interactions, and just 18 months later surpassed 2 billion interactions in support of 42 million customers, the company reported in an April 2024 news release.
“Erica acts as both a personal concierge and mission control for our clients,” Nikki Katz, head of digital at Bank of America, said in the same news release. “Erica meets clients where they are and when they need us, and has become a true guide by their side.”
And Erica is only getting smarter.
“The bank is continually fine-tuning and feeding the tool new information to better respond to customer inquiries,” Banking Dive reported last year. “In light of its popularity with clients, Bank of America has sought to integrate the tool more deeply into the bank’s digital functions.”
4. Credit Risk Analysis
Lending platform Upstart uses AI to assess loan applicants’ creditworthiness. Traditional credit models can unintentionally shut the door on creditworthy borrowers, and the company says its AI-assisted efforts aren’t just about maximizing bank profits. They’re also aimed at addressing social justice concerns.
“AI-powered underwriting models not only discern a borrower’s true creditworthiness, but ensure that underserved communities are no longer overlooked and instead provided with equitable opportunities for financial growth,” Upstart notes on its website.
At the other end of the spectrum, Upstart last fall announced the launch of T-Prime, tapping AI to connect banks and credit unions with the most well-heeling clients.
“We’ve historically focused our AI platform on underserved consumers,” co-founder and CEO Dave Girouard said in an October 2024 news release.
“But with our expansion into T-Prime, we’re helping our bank and credit union partners leverage their competitive cost of funding to win the business of our nation’s more affluent borrowers.”
5. Financial Reporting
In support of reporting and compliance, the cloud-based and AI-enhanced platform OneStream automates financial processes across a range of industries.
“Our packaged applied AI solutions are purpose-built for finance,” the company explains on its website.
The platform enables organizations to automate core tasks, empowering the financial team with accurate reporting in support of strategic decision-making. Costco, for example, has used OneStream to improve cashflow reporting, generate key metrics and gain visibility into business trends. This helps to establish annual budgets and supports other key financial functions.
O’Dowd pointed to UBS's Red platform. The system, built using Azure AI Search and Azure OpenAI Service, provides multi-language capabilities for client advisers accessing the bank's knowledge base. It helps reduce time spent on meeting preparation and research.
"The internal information that we have, the financial expertise, the insights into financial markets, what's happening in the world, it's vast,” Michel Neuhaus, head of AI, Data & Analytics of Personal & Corporate Banking at UBS, told FinTech magazine. “We have to find ways to make this content easily accessible to each and everyone in the bank, and Microsoft helps us with that."
The bank's AI transformation extends beyond UBS Red to include a comprehensive modernization of its data platform.
“While delivering flagship AI solutions like Red is crucial, it's equally important to build a scalable organisation that can accelerate the delivery of AI use cases,” Jonas Isliker, Head of AI, Data & Analytics of Global Wealth Management at UBS, told FinTech. “This involves implementing a data mesh, moving to the Azure cloud, establishing new AI risk governance, and creating AI factories for quicker, more agile solution delivery.”
Implications for Other Industries
In virtually every sector of the economy, businesses are looking to explore the benefits of artificial intelligence. Although each faces its own unique AI challenges and opportunities, there’s much to be learned from the use cases that are flourishing in financial services, according to O’Dowd.
It’s worth noting, for example, that financial services has embraced AI with specific business goals in mind.
“Banks and other financial organizations are tied to the idea of a business-driven AI journey,” O’Dowd said. “It’s not just use cases for their own sake. They are linking the technology to the business imperative. If you’re a bank and you want to improve deposits, maybe AI can help there, maybe it can’t. But you start from that position of making it a business-driven journey.”
In other industries, the same laser-focus on business needs and practical use cases can help maximize the benefits as companies look to implement AI-driven solutions.
Still, AI comes with some inherent risk. With that in mind, one reason the financial services sector has succeeded with AI is because it has implemented it alongside robust internal controls.
“They’re able to march forward in an organized fashion because they have great risk management procedures and processes,” concluded O’Dowd, who said other industries would do well to follow suit by putting in place strong governance to ensure the appropriate and effective use of AI-driven applications.
Editor’s note: Learn how Nutanix software powers hybrid multicloud, Finserv's optimal IT model, simplifies cloud adoption, speeds AI implementation, and modernizes apps for banks, capital markets, insurers, payments providers, and fintech.
Adam Stone is a journalist with more than 20 years of experience covering technology trends in the public and private sectors.
Ken Kaplan contributed to this story. He is Editor in Chief for The Forecast by Nutanix. Find him on X @kenekaplan and LinkedIn.
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