The financial services industry is one of the most regulated sectors. It also manages huge amounts of data. Conscious of a need for caution, financial companies have slowly added generative AI and AI agents to their stables of services.
The industry is no stranger to automation. But use of the term “agent” has been muted. And understandably, many in the industry took a very cautious stance toward generative AI, especially in the absence of regulatory frameworks. Now, however, banks like JP Morgan and Bank of America have debuted AI-powered assistants.
A bank at the forefront of the trend is BNY. The investment and custodian bank founded by Alexander Hamilton is updating its AI tool, Eliza (named after Hamilton’s wife), developing it into a multi-agent resource. The bank sees AI agents as providing valuable assistance to its sales representatives while engaging its corporate customers more.
A multi-agent approach
Saarthak Pattanaik, head of BNY’s Artificial Intelligence Hub and head of engineering for digital assets, treasury, clearance and control, told VentureBeat in an interview that the bank began by figuring out how to connect its many units so their information can be easily accessed.
BNY created a lead recommendation agent for its various teams. But it did more. In fact, it uses a multi-agent architecture to help its sales team make suitable recommendations to clients.
“We have an agent which has everything [the sales team] know[s] about our client,” Pattanaik said. “We have another agent which talks about products, all the products that the bank has…from liquidity to collateral, to payments, the treasury and so forth. Ultimately…we are trying to solve a client need through the capabilities we have, the product capabilities we have.”
Pattanaik added that its agents have reduced the number of people many of its client-facing employees must speak to in order to determine a good recommendation for customers. So, “instead of the salespeople talking to 10 different product managers, 10 different client people, 10 different segment people, all of that is done now through this agent.”
The agent lets its sales team answer very specific questions that investment banking clients might have. For example, does the bank support foreign currencies like the Malaysian ringgit if a client wants to launch a credit card in the country?
How they built it
The multi-agent recommendation capabilities debuted in BNY’s Eliza tool.
There are about 13 agents that “negotiate with each other” to figure out a good product recommendation, depending on the marketing segment. Pattanaik explained that the agents range from functional agents like client agents to segment agents that touch on structured and unstructured data. Many of the agents within Eliza have a “sense of reasoning.”
The bank understands that its agent ecosystem is not fully agentic. As Pattanaik pointed out, “the fully agentic version would be that it would automatically generate a PowerPoint we can give to the client, but that’s not what we do.”
Pattanaik said the bank turned to Microsoft’s Autogen to bring its AI agents to life.
“We started off with Autogen since it is open-source,” he said. “We are generally a builder company; wherever we can use open source, we do it.”
Pattanaik said Autogen provided the bank with a set of solid guardrails it can use to ground many of the agents’ responses and make them more deterministic. The bank also looked into LangChain to architect the system.
BNY built a framework around the agentic system that gives the agents a blueprint for responding to requests. To accomplish this, the company’s AI engineers worked closely with other bank departments. Pattanaik underscored that BNY has been building mission-critical platforms for years and has scaled products like its clearance and collateral platforms. This deep bench of knowledge was key to helping the AI engineers in charge of the agent platform give the agents the specialized expertise they needed.
“Having less hallucination is a characteristic that always helps, compared to just having AI engineers driving the engine,” Pattanaik said. “Our AI engineers worked very closely with the full-stack engineers who built the mission-critical systems to help us ground the problem. It’s about componentizing so that it’s reusable.”
Building, for example, a lead-recommendation agent this way allows it to be developed by BNY’s different lines of business. It acts as a microservice “that continues to learn, reason and act.”
Expanding Eliza
As its agentic footprint expands, BNY plans to further upgrade its flagship AI tool, Eliza. BNY released the tool in 2024, though it has been in development since 2023. Eliza lets BNY employees access a marketplace of AI apps, get approved datasets and look for insights.
Pattanaik said Eliza is already providing a blueprint for how BNY can move forward with AI agents and offer users more advanced, intelligent service. But the bank doesn’t want to be stagnant, and wants the next iteration of Eliza to be more intelligent.
“What we built using Eliza 1.0 is a representation, and the learning aspect of things,” Pattanaik said. “With 2.0, we’re going to improve the process and also ask, how do we build a great agent? If you think about agents, it’s about something that can learn and reason and, at some point in time, provide some actions as to this is a break, this is not a break and so forth. This is the direction we are going towards as we build 2.0, because a lot of things have to be set up in terms of the risk guardrails, the explainability, the transparency, the linkages and so forth, before we become completely autonomous.”
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