The financial industry’s approach to artificial intelligence reveals considerable pragmatism.
Popular notions of generative AI, guided by the explosive growth of OpenAI’s ChatGPT, often center on consumer-facing chatbots. But financial institutions are leaning more heavily on internal AI tools that streamline day-to-day tasks.
This requires training programs and user-experience design that help a bank’s entire organization — from relationship bankers directing high-value accounts to associates — understand the latest AI technology.
From AI classification to AI generation
Banks have long used traditional AI and machine learning techniques for various functions, such as customer service bots and decision algorithms that provide a faster-than-human response to market swings.
But modern generative AI is different from prior AI/ML methods, and it has its own strengths and weaknesses. Hari Gopalkrishnan, Bank of America’s chief information officer and head of retail, preferred, small business, and wealth technology, said generative AI is a new tool that offers new capabilities, rather than a replacement for prior AI efforts.
“We have a four-layer framework that we think about with regards to AI,” Gopalkrishnan told Business Insider.
The first layer is rules-based automation that takes actions based on specific conditions, like collecting and preserving data about a declined credit card transaction when one occurs. The second is analytical models, such as those used for fraud detection. The third layer is language classification, which Bank of America used to build Erica, a virtual financial assistant, in 2016.
“Our journey of Erica started off with understanding language for the purposes of classification,” Gopalkrishnan said. But the company isn’t generating anything with Erica, he added: “We’re classifying customer questions into buckets of intents and using those intents to take customers to the right part of the app or website to help them serve themselves.”
The fourth layer, of course, is generative AI.
Koren Picariello, a Morgan Stanley managing director and its head of wealth management generative AI, said Morgan Stanley took a similar path. Throughout the 2010s, the company used machine learning for several purposes, like seeking investment opportunities that meet the needs and preferences of specific clients. Many of these techniques are still used.
“Historically, I was working in analytics, data, and innovation within the wealth space. In that space, Morgan Stanley did focus on the more traditional AI/ML tools,” Picariello told BI. “Then in 2022, we started a dialogue with OpenAI before they became a household name. And that began our generative-AI journey.”
How banks are deploying AI
Given the history, it’d be reasonable to think banks would turn generative-AI tools into new chatbots that more or less serve as better versions of Bank of America’s Erica, or as autonomous financial advisors. But the most immediate changes instead came to internal processes and tools.
Morgan Stanley’s first major generative-AI tool, Morgan Stanley Assistant, was launched in September 2023 for employees such as financial advisors and support staff who help clients manage their money. Powered by OpenAI’s GPT-4, it was designed to give responses grounded in the company’s library of over 100,000 research reports and documents.
The second tool, Morgan Stanley Debrief, was launched in June. It helps financial advisors create, review, and summarize notes from meetings with clients.
“It’s kind of like having the most informed person at Morgan Stanley sitting next to you,” Picariello said. “Because any question you have, whether it was operational in nature or research in nature, what we’ve asked the model to do is source an answer to the user based on our internal content.”
Bank of America is pursuing similar applications, including a call center tool that saves customer associates’ time by transcribing customer conversations in real time, classifying the customer’s needs, and generating a summary for the agent.
Keeping humans in the loop
The decision to deploy generative AI internally first, rather than externally, was in part due to generative AI’s most notable weakness: hallucinations.
In generative AI, a hallucination is an inaccurate or nonsensical response to a prompt, like when Google Search’s AI infamously recommended that home chefs use glue to keep cheese from sliding off a pizza.
Banks are wary of consumer-facing AI chatbots that could make similar errors about bank products and policies.
Deploying generative AI internally lessens the concern. It’s not used to autonomously serve a bank’s customers and clients but to assist bank employees, who have the option to accept or reject its advice or assistance.
Bank of America provides AI tools that can help relationship bankers prep for a meeting with a client, but it doesn’t aim to automate the bank-client relationship, Gopalkrishnan told BI.
Picariello said Morgan Stanley takes a similar approach to using generative AI while maintaining accuracy. The company’s AI-generated meeting summaries could be automatically shared with clients, but they’re not. Instead, financial advisors review them before they’re sent.
Training the finance workforce for AI
Bank of America and Morgan Stanley are also training bank employees on how to use generative-AI tools, though their strategies diverge.
Gopalkrishnan said Bank of America takes a top-down approach to educating senior leadership about the potential and risks of generative AI.
About two years ago, he told BI, he helped top-level staff at the bank become “well aware” of what’s possible with AI. He said having the company’s senior leadership briefed on generative AI’s perks, as well as its limitations, was important to making informed decisions across the company.
Meanwhile, Morgan Stanley is concentrating on making the company’s AI tools easy to understand.
“We’ve spent a lot of time thinking through the UX associated with these tools, to make them intuitive to use, and taking users through the process and cycle of working with generative AI,” Picariello said. “Much of the training is built into the workflow and the user experience.” For example, Morgan Stanley’s tools can advise employees on how to reframe or change a prompt to yield a better response.
For now, banks are focusing AI initiatives on identifying and automating increasingly more complex and nuanced tasks within the organizations rather than developing one-off applications targeted at the customer experience.
“We try to approach problems not as a technology problem but as a business problem. And the business problem is that Bank of America employees all perform lots of tasks in the company,” said Gopalkrishnan. “The opportunity is to think more holistically, to understand the tasks and find the biggest opportunities so that five and 10 years from now, we’re a far more efficient organization.”
