David Hickey is the principal of Digital Solutions Practice at Baker Tilly's, an advisory tax and guarantor company based in Chicago, Illinois. The view is the author himself.
Financial institutions are leveraging artificial intelligence agents to become more streamlined, efficient and automated.
It is used by a variety of financial sectors, whether it be banks and credit unions, capital markets, insurance or asset management. agent AI in a similar way.
Agent AI represents a sophisticated form of autonomous AI that mimics human cognitive processes. These AI “agents” are designed to make decisions, collaborate, and adapt independently. By ensuring that agents are trained with a focus on very specific tasks, they can work reliably and safely autonomously. As you learn and optimize the functionality, these agents significantly improve efficiency, save time, and streamline complex workflows that require traditional human intervention.
Building Agent Persona
In the field of financial services, developing agent personas is important to leverage agent AI to maximize its potential. Agent personas are digital representations of different roles within an organization, designed to perform specific tasks and interact with both systems and humans in ways that mimic actual interactions. Think of it as a combination of job descriptions and standard operating procedures for the person performing the task.
These personas start with a well-defined system prompt. This includes identifying the specific tasks and responsibilities associated with the role, as well as the skills and knowledge required to effectively carry them out. Like human employees, agent personas must have a defined personality that matches the brand and values of the organization. This is especially true for agents who interact with people outside their organization, such as determining the tone and style of communication, as well as the level of form and empathy that the agent should exhibit.
Once a role is defined, there is additional context for how the role is performed. Context grounding is essential for various applications within intelligent automation. For example, the financial industry sets parameters for AI agents' database access, standard operating procedures, regulatory information, and other trusted sources. This ensures that agents are always up to date with the latest information and understand how to leverage that information in task performance.
By grounding the AI model in a specific business context, organizations can ensure that the AI system is not only accurate, but also aligns with their own operational needs and regulatory requirements.
After defining roles and personality, providing the context for the data source, it is time to define specific boundaries that agents are allowed to operate. This involves supplying an agent-related use case or scenario and observing its response. Use unexpected responses to improve system prompts to facilitate proper behavior.
Modern prompt building tools evaluate that use cases are based on defined roles and recommend additional use cases that help to enhance prompts. It leverages external black hat agents to help test boundaries provided to agents and improve system prompts.
As agents perform tasks, continuous training and development is essential to ensure that agents are up to date with the latest information and handle new and evolving tasks. This includes tracking key performance indicators, gathering user feedback, and identifying areas for improvement. Regular assessments help to ensure that agents are achieving their goals within the overall context of their role.
Adjust complex workflows
In the dynamic world of financial services, the ability to model and manage complex, long-term workflows is extremely important. Recent advances in technology have made this possible, allowing organizations to integrate agents, bots and humans into seamless, end-to-end processes.
However, managing long-term workflows that touch multiple subprocesses and systems presents several challenges. One of the main challenges is to ensure that each step in the process is carried out accurately and efficiently. This requires meticulous adjustments and monitoring at the transaction level to prevent errors and delays. Furthermore, the complexity of integrating different systems and tools can lead to compatibility issues and data silos, which can hinder the smooth flow of information.
Provide an orchestration layer to seamlessly integrate various agent, bot, and human tasks into a unified workflow. This orchestration allows each component of the process to function harmoniously, reducing bottlenecks and increasing overall efficiency. By adjusting and monitoring each step, the orchestration layer prevents errors and delays, ensuring that the entire workflow works smoothly and effectively.
For example, within a loan application, an agent can perform an initial review of the document, automatically requesting clarification from the missing information or applicants, and start the next step of checking credit, verifying income and employment, and predicting possible defaults for the loan. The completed package can then be sent to the underwriter for review, and once approved, the agent will generate a loan offer immediately and send it to the applicant.
This transaction-level concierge service advances the transaction through the process as soon as the process steps are completed, ensuring unnecessary delays and quick execution of each step. Agents can only request human support when needed, allowing human agents to focus on more complex, strategic tasks, or provide more time for direct interaction with customers. This approach not only increases workflow efficiency, but also ensures that the overall process is smooth and sensitive to the needs of the customer.
One of the most important benefits of central orchestration is its ability to maintain an overall context of what is happening within the process. As transactions move seamlessly between agents, bots and people, a central orchestration maintains context throughout. This means that each component of the workflow recognizes previous steps and overall purpose, allowing for more informed decisions. This continuity is important to maintain the integrity and efficiency of the process.
This visually tuned, end-to-end process flow may also provide a level of visibility and accountability that audit and compliance departments can take advantage of. A comprehensive view of the entire workflow allows organizations to ensure that every step of the process is transparent and traceable. This transparency not only improves accountability, but also allows for improved process decision-making and optimization.
Additionally, the ability to visualize the entire process flow allows for continuous improvement and optimization. Organizations can analyze the data collected from these workflows to identify bottlenecks, inefficiencies, and areas for improvement. By making data-driven decisions, organizations can enhance processes, reduce costs and improve overall performance.
The road ahead
As AI technology continues to advance at an exponential rate, the impact of agent AI is set to become even more severe. These intelligent agents are increasingly sophisticated, able to handle complex tasks and make more accurate and subtle decisions.
Despite the current situation of different systems and applications of AI, the future holds immeasurable promises. The financial services industry stands on the brink of a transformational era driven by the power of agent AI.
