AI Agents and the Transformation of the Financial Industry

Early Adoptions of AI Agents in Financial Services

AI agents are leading the digital transformation in the financial industry. Evolving from traditional rule-based AI to large language model (LLM)-based generative AI, they are at the forefront of change. While the financial sector is still in the learning phase, several early-use cases have been successfully implemented. Below are three such examples.

Moody’s – Financial Analysis Agent

Moody’s developed 35 agents, including specialized AI agents for tasks like project management, and integrated them into a “multi-agent system” where they collaborate with supervising agents(2). This system is used to analyze SEC filings and industry data, enhancing their research capabilities. The AI agents are specialized in specific tasks, and by having other agents verify the results, they can efficiently execute complex workflows.

Auquan – Financial Industry-Specific Digital Analyst

The automation of analyst tasks using AI agents is progressing. The startup Auquan developed an AI agent that specializes in creating financial industry reports, which is different from OpenAI’s “Deep Research,” designed for general purposes(3). For example, it collects and analyzes financial data (including unstructured data such as presentations and PDFs) and internet information to generate reports. This method is 80–90% cheaper and significantly more productive than manual report generation. Clients include MetLife, UBS, and Capital Group. Auquan has announced plans to extend its AI solutions to risk management, compliance, and sustainability workflows, and it is believed that these are now being implemented.

Capital One – Chat Concierge Agent

Capital One has introduced a chat concierge that uses multiple AI agents to comprehensively address customer inquiries related to car purchases(4). The concierge supports the entire purchase process, from vehicle comparison to test-drive reservations, easing the customer’s burden. With a customized Llama AI-based model, it responds instantly to customer questions and can perform multiple tasks at once, such as providing trade-in estimates and setting appointments with sales representatives.

BNY– The Approach to Multi-Agent Systems

BNY has implemented a multi-agent system that includes 13 specialized agents, providing sales representatives with lead generation and recommendations through AI agents. The AI tool “Eliza” quickly understands client needs, improving sales efficiency, while multiple agents collaborate to produce the best outcomes(5). In addition, through a partnership with OpenAI, BNY is integrating new features into Eliza via an API, enabling the resolution of complex tasks and the acquisition of insights. By leveraging cutting-edge reasoning models and agent functions (Deep Research, Operator), BNY expects to enhance Eliza’s accuracy and autonomy(6).

Building on the discussions thus far, the evolution of AI’s natural language understanding is making human-machine interactions increasingly intuitive and flexible. Against this backdrop, we can envision a future where AI agents autonomously execute business processes, driving the transformation of financial services. However, the introduction of AI agent systems also brings potential challenges, such as an increase in systemic risks and volatility in financial markets, disruptions in the labor market, and the emergence of new security risks. Additionally, ethical and privacy concerns, as well as the opacity of outcomes resulting from autonomous decision-making, are important considerations. Therefore, it is essential to balance the pursuit of AI agent autonomy in financial services with addressing the challenges that arise from it.

In reality, the role of humans in financial services will need to be redefined in the age of AI agents. Specifically, the role of humans will shift from being “operators” of daily tasks to “orchestrators” who oversee and adjust AI systems. Humans will monitor the progress of business operations and intervene or adjust when necessary. As advancements are made in areas like improved reasoning abilities, hallucination mitigation, and the reduction of ethical and privacy risks, human intervention will gradually become minimal. Furthermore, humans are expected to focus more on creative tasks that leverage their emotional intelligence, cognitive skills, and physical dexterity, working in collaboration with AI to expand these capabilities.

The rapid innovation of AI agent technology requires the financial services industry to set value-driven goals, enhance the necessary skills and capabilities, and establish a robust governance framework that includes change management and risk management. By making these preparations, we can realize a future in which humans and AI agents work together to shape the financial services of tomorrow.