As it turns out, Wall Street banks and other financial giants alike are no strangers to using ChatGPT to gain an advantage. At the latest Money 20/20 fintech conference, held in June 2023 in Amsterdam, Netherlands, executives at global banks and digital finance firms praised generative AI, naming it an “explosion of innovation” and claiming it will “unleash innovation in areas that we can’t even think about.”
Generative AI technology, including large language models, is gaining interest among banks, and they are enthusiastically experimenting with ChatGPT-style solutions, exploring its potential in various areas.
We have compiled a list of noteworthy examples of banks adopting generative AI in 2023. These examples serve as compelling illustrations of the benefits of generative AI in the financial sector and will help you navigate your business through the era of AI.
As financial institutions recognize the disruptive impact of generative AI, they are currently actively running proofs-of-concept (POCs) and experiments with language models:
- MORGAN STANLEY WEALTH MANAGEMENT: Exploiting the vast repository of data for better customer service
Morgan Stanley, an American multinational investment bank, is launching an advanced chatbot powered by OpenAI’s most recent technology to help the company’s team of financial advisors. The idea behind the tool, which has been under development for a year, is to assist the bank’s 16,000 or so advisors in leveraging the bank’s massive research and data library.
It will provide financial advisors and their teams with the capability to ask questions and analyse large amounts of content and data, with answers derived 100% from MSWM content and links to the source materials.
“This technology is a game changer in synthesizing our expansive intellectual capital, bringing the value and richness of it to a whole new level, and in the process freeing up valuable time for Financial Advisors to do what they do best—serve their clients,” said Andy Saperstein, Co-President and Head of Morgan Stanley Wealth Management.
“People want to be as knowledgeable as the smartest person,” said Jeff McMillan, head of analytics and data. “This is like having our chief strategy officer sitting next to you when you’re on the phone with a client.”
- ABN AMRO: Summarising conversations between bank staff and customers
The Netherlands’ banking giant, ABN Amro, is also piloting the use of generative AI in its processes.
Traditionally, bank agents have taken notes during a customer call so they can produce a summary later. The bank now relies on ChatGPT to generate these summaries, with the agents simply monitoring accuracy afterwards.
“Employees using the technology had said that they now have more time to focus on the client, without the need to “scribble down notes,” said Annerie Vreugdenhil, Annerie Vreugdenhil, chief commercial officer of ABN Amro’s personal and business banking division.
Recently, ABN has scaled up the use of Generative AI to produce summaries of product pages. “There are lots of these pages, which agents don’t know off by heart, which means the technology reduces time spent on trying to find a particular product,” added Vreugdenhil.
- GOLDMAN SACHS: Helping developers write code
Over the last few years, Goldman Sachs has invested heavily in making the bank a more technology-driven organization, including the launch of a digital bank in the United Kingdom in 2018.
This year Goldman Sachs is experimenting with generative AI tools to help its software engineers automatically generate lines of code.
As Goldman’s innovation chief, Marco Argenti, emphasised, artificial intelligence should not be viewed as a replacement for software developers but rather as a companion. Using generative AI, developers have been able to automate 40 per cent of their code. They also use it for testing.
“If you actually have a GPT-like technology that tests the code, or you generate the tests for the GPT code, you’re creating this dualism where you test the machine and you get the machine to test your work,” Argenti said.
- SOUTHSTATE BANK: Empowering knowledge sharing and productivity
SouthState Bank, located in Florida, USA, has enthusiastically adopted a language solution trained on bank documents and data to enable employees to query the system and summarise and interpret the bank’s internal records. Thanks to this, new employees are promptly becoming proficient in specific topics or regulations.
“My team uses it for about 50% of the searches it used to use Google for. It’s far superior in a lot of ways. The difference is that you can drill down and refine your prompt, which you can’t do with Sinippets,” said Chris Nichols, Director of Capital Markets at SouthState Bank.
The tool also helps staff perform various tasks, including composing emails, generating expense reports, analyzing suspicious activity, and analyzing fraud. Since introducing the solution, SouthState Bank has witnessed a substantial boost in productivity. For instance, tasks that previously took an average of 12 to 15 minutes now take mere seconds.
“We use it all the time to summarize a set of policies or a regulatory document or to compose emails or to help marketing create copy,” Nichols said. In his recent LinkedIn post, Chris even shared 15 use cases of ChatGPT in banking with his community.
- WESTPAC: Assisting borrowers and loan officers with the mortgage process
A home loan application involves a lot of paperwork. Applicants must fill out many forms to qualify, so the bank needs to acquire and validate various information to approve the loan.
To streamline the process and help lending staff and customers, Sydney-based bank Westpac decided to deploy a language model trained on conversations and data in the banking industry for internal use.
“This is going to aid us in checking the quality of information coming in, so it’s going to stop us having to go backwards and forwards to our customers,” said David Walker, chief technology officer of Westpac. “it’s going to make things much more straight through and seamless.”
Other banks are also experimenting with ChatGPT-style technology. For instance, JPMorgan Chase is utilising it to analyse emails for signs of fraud, Wells Fargo is using an LLM to help specify what information clients must provide to regulators. Employees at the Swedish buy now, pay later fintech company are provided with a ChatGPT-4 account and encouraged to experiment with the new technology.
Amidst the boom of experiments with generative AI in finance that are currently taking place, one question that naturally arises is:
What are predictions for the rest of 2023 and beyond?
The use of AI is not new to the industry and has been used for automating tasks of trading, risk management, and investment research. But when the new generation of large language models emerged on the horizon and showed a great potential for enhancing worker performance, Generative AI seems to have become pursuit sought by all financial institutions.
Deloitte’s analysis fuels the enthusiasm suggesting that the use of generative AI can boost productivity for front-office employees in global banks by as much as 27%–35% by 2026. This boost in productivity carries the potential to generate additional revenue of up to US$3.5 million per front-office employee by the same year. The possibilities are exciting, to say the least, but Generative AI adoption is going to be a gradual process.
For now, for internal use only. Peter Wannemacher, principal analyst at Forrester, predicts that other banks will conduct similar experiments over the next two years. However, they’ll keep its use strictly internal and won’t launch anything customer-facing until they have a much better sense of the technology. “Most traditional financial institutions will start by focusing on employee-facing generative tools, rather than exposing a chatbot built on top of a large language model directly to the end user,” Wannemacher said.
Scaling up the adoption. If the ongoing generative AI LLM pilot projects are successful, banks are likely to expand the technology to other departments and activities. Thus, after seeing the first positive results, ABN Amro decided to scale their conversation summary automation pilot to 200 employees in their contact centre and is examining several new use cases and pilots to start this summer.
| Delve deeper into generative AI use cases in banking in our latest 17 ChatGPT Use Cases for Banks and Finance blog article.
Creating new AI-related jobs. The adoption of LLM-bsaed solutions are predicted to drive more AI-related hires. In fact, from February to April 2023 alone, JPMorgan advertised more than 3,600 AI-related jobs. Most of them data engineers and quants, and ethics roles.
Overall, the trajectory of generative AI in finance appears promising, with the potential for enhanced productivity, improved profits, and the creation of new job roles to fuel further advancements.
Tovie AI perspective
“Conversational AI is becoming a foundational technology not only for customer-facing applications but also for internal use. The new conversational experience in the workplace is highly beneficial – employees can now ask questions and get information in a conversational format from across different data sources spread across the business in a matter of seconds, saving time and effort on document search. Language models have the potential to become the primary means of interaction between employees and organisational knowledge. This will lead to scaling expertise and productivity improvements within an organisation.”
Joshua Kaiser, CEO at Tovie AI
If you want to seize the opportunity to be an early adopter in Generative AI for business, kickstart your journey by engaging with our dedicated ChatGPT Consulting services. Our AI Readiness team will help you understand Generative AI and how it can be used for the digital transformation of your business. We assess factors such as infrastructure, data availability, and technical ability, helping define use cases and potential outcomes. As a result, we’ll help you design a successful Generative AI adoption strategy to enable your business:
- Organise institutional knowledge
- Scale employee expertise
- Foster employee collaboration
- Improve productivity
- Provide better customer service
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