The financial services sector is on the brink of a significant transformation, driven by the rapid advancements in artificial intelligence (AI). Among the various AI technologies, agentic AI stands out as a game-changer, promising to revolutionize fintech and banking through its autonomous decision-making capabilities. The inception of agentic AI, which involves creating intelligent AI “agents” capable of performing tasks autonomously, is set to tackle some of the most pressing challenges faced by the industry. Unlike traditional AI models that focus solely on data outputs, agentic AI incorporates sensory perceptions of the environment and actuators to respond to those inputs, creating a more dynamic and interactive system. This leap beyond simple data processing not only provides data but also enables agentic AI to act autonomously within an environment, tackling multifaceted problems and automating intricate processes.
Defining Agentic AI
Agentic AI transcends traditional AI models by incorporating sensory perceptions and actuators, thus creating a more dynamic and interactive system. This innovative technology is not only limited to producing data but has the capability to act autonomously within an environment, solving multifaceted problems and automating intricate processes. Experts in the field emphasize the potential of agentic AI to revolutionize the financial services sector by enabling fully automated processes that were once considered too arduous or complex.
Matt Roberts, head of data at ClearBank, describes agentic AI as a leap beyond simple data processing. He explains that it automates entire processes that require sophisticated decision-making capabilities. The catalyst for this technology’s evolution has been advancements in large language models (LLMs) and generative AI (GenAI). These advancements have created a fertile ground for autonomous agents to thrive, allowing the financial sector to embrace this transformative force.
Recent Developments
Several notable companies and partnerships underscore the growing momentum of agentic AI in the financial services sector. For instance, BNP Paribas has entered a multi-year partnership with Mistral AI to leverage AI for its operations. Similarly, BBVA is deploying ChatGPT technologies via OpenAI, and Franklin Templeton is collaborating with Microsoft to build an advanced financial AI platform. These partnerships reflect the industry’s robust appetite for AI-driven solutions and foreshadow the remarkable impact agentic AI could have if developed and harnessed correctly.
Google is another prime example of a heavyweight diving into agentic AI, heralding the launch of Gemini 2.0. CEO Sundar Pichai envisions it as a step toward creating a universal AI assistant. In January, OpenAI introduced its first AI agent, Operator, capable of performing web tasks, signaling a significant milestone. These developments highlight the rapid pace of innovation and the enthusiastic reception of agentic AI tools in the financial services sector.
Agentic AI in Financial Services
Agentic AI promises to transform financial services by automating historically labor-intensive and complex processes seamlessly. Fouzi Husaini, CTO and chief AI officer at Marqeta, equates the advent of agentic AI to having an unlimited number of “really smart interns” capable of handling tedious and manual backend operations. While technology is still in its infancy, Husaini notes its exciting potential, particularly for fintech companies. By automating documentation and process adherence, banks can achieve higher efficiency and accuracy, reducing the workload on human staff and minimizing the risk of errors.
Maslaveckas envisions future banks as clusters of agentic models overseen by human staff to ensure reliability and prevent erroneous actions. This transformation could lead to significant improvements in operational efficiency and customer experience. The rapid pace of innovation and the enthusiastic reception of agentic AI tools suggest a swift climb toward greater commercial viability. Experts predict that developers will continue their experimental phase into early 2025, with tangible commercial breakthroughs anticipated toward the end of the current year and into the next.
Future Outlook and Industry Dynamics
The rapid pace of innovation and the enthusiastic reception of agentic AI tools suggest a swift climb toward greater commercial viability. Developers are expected to continue their experimental phase into early 2025, with tangible commercial breakthroughs anticipated toward the end of the current year and into the next. However, the industry’s progression towards widespread adoption must be tempered by careful consideration of regulatory frameworks to manage risks.
The EU AI Act emphasizes the need for explainability and human oversight, marking a proactive stance by regulators. Nonetheless, balancing innovation with regulation remains a critical challenge for the industry. Edward Maslaveckas, CEO and co-founder of Bud Financial, warns against overly stringent regulations that could stifle innovation. He argues that regulators should encourage AI advancement rather than deter it with burdensome regulations. Maslaveckas cites the UK Financial Conduct Authority’s AI Lab as a positive step in fostering AI-driven innovation within a regulated framework. Additionally, the changing regulatory attitudes in the US underscore the need for a balanced approach.
Practical Applications and Benefits
Agentic AI holds promise for both consumer-facing and back-office applications in financial services. Maslaveckas highlights an agentic capability introduced by Bud Financial in September, combining their data intelligence platform with a consumer agent to recommend and execute tailored financial products automatically. This innovation aims to reduce consumer apathy toward financial product decisions by identifying opportunities and automating recommendations.
Moreover, agentic AI can significantly impact internal operations in banks and financial institutions. By automating documentation and process adherence, banks can achieve higher efficiency and accuracy, reducing the workload on human staff and minimizing the risk of errors. This transformation could lead to significant improvements in operational efficiency, customer experience, and overall service quality. The integration of agentic AI promises to streamline operations, enhance decision-making processes, and deliver a higher level of service to clients.
Conclusion
Several prominent companies and partnerships emphasize the increasing drive towards agentic AI within the financial services industry. For example, BNP Paribas has forged a multi-year partnership with Mistral AI to integrate AI into its operations. Similarly, BBVA is implementing ChatGPT technologies through a collaboration with OpenAI, while Franklin Templeton teams up with Microsoft to develop an advanced financial AI platform. These alliances demonstrate the sector’s strong interest in AI solutions, hinting at the substantial impact that properly developed and utilized agentic AI could bring.
Google also exemplifies a major player entering the agentic AI arena, with the unveiling of Gemini 2.0. CEO Sundar Pichai sees this as a move towards creating a universal AI assistant. In January, OpenAI launched its inaugural AI agent, Operator, capable of executing web tasks, marking a significant milestone. These strides underscore the rapid innovation and eager adoption of agentic AI tools in the financial services sector, showcasing a vibrant landscape of technological advancements.