Agentic AI Revolutionizes Financial Services: Issues and Challenges

The Crucial Importance of Data in Agentic AI

In the financial services sector, the integration of agentic artificial intelligence presents a unique challenge. This industry, among the most strictly regulated, must constantly adapt to external events that evolve at every moment. The success of agentic AI in this field relies less on the complexity of the systems and more on the quality, security, and accessibility of the data it uses.

Steve Mayzak, Global Managing Director of Search AI at Elastic, emphasizes that it all starts with data. Agentic AI, distinguished by its ability to plan and act autonomously to accomplish tasks, offers considerable potential in financial services. It can integrate real-time data and optimize complex processes. According to Gartner, more than 50% of financial services teams have already implemented or are considering implementing agentic AI.

However, the introduction of autonomous AI into an organization can exacerbate both the strengths and weaknesses of the data it utilizes. To deploy agentic AI effectively and confidently, companies must first be able to search for, secure, and contextualize their data at scale. As Mayzak points out, agentic AI amplifies the weakest link in the chain, namely the availability and quality of data. Systems cannot be better than their weakest link.

Information Quality: A Major Challenge

In the financial sector, regulations impose a high level of accountability for all data tools. Mayzak explains that companies cannot simply show where the data comes from and what happens to it. It is essential to be able to explain in an auditable and manageable way which information the model used and why it was appropriate for the next step.

Financial services companies must also demonstrate speed and accuracy to meet customer expectations and remain competitive. Markets are constantly evolving, and risks and opportunities evolve with them. If an AI model can analyze natural language from complex sources, in addition to structured data, it provides more relevant insights to users.

In this environment, there is no room for error, particularly the hallucinations that have affected early AI attempts. Agentic AI systems require quick access to high-quality, well-governed, secure, and accessible data. In financial services, this data includes transactions, customer interactions, risk signals, policies, and historical context. Preparing this data for AI is a complex yet crucial task.

Data must be well-indexed and consolidated across different locations, rather than siloed in separate systems within the organization. Otherwise, AI agents risk falling behind, providing inconsistent responses, and producing decisions that are difficult to trace and explain, undermining the trust of regulators, customers, and internal stakeholders.

Mayzak emphasizes that there are many ways to describe the execution of a transaction in a bank. In a world powered by agents, these descriptions must be deterministic to yield the same results every time. Yet, we rely on powerful but non-deterministic models, which is tricky but not impossible. For a financial services company, managing this can be very challenging. A Forrester study revealed that 57% of financial organizations are still developing the internal capabilities needed to fully leverage agentic AI.

Searching and Securing Outcomes

An effective search platform is essential to address the problem of fragmented, poorly indexed, and inaccessible data. Financial services companies that can easily sort both their structured and unstructured data, keep it secure, and apply it in the right context will derive the most value from agentic AI. This often requires designing AI systems with data access and utility in mind so they can operate faster and produce more accurate results while reducing risks.

Mayzak asserts that search is the foundational technology that makes AI accurate and grounded in real data. Search platforms become the context and authoritative memories that will fuel this AI revolution. Once in place, these AI-enhanced searches and autonomous systems can serve financial services companies for a range of needs.

When monitoring customer exposure, agentic AI can continuously scan transactions, market signals, and external data to detect emerging risks. Platforms can then automatically flag or escalate issues in real-time. In transaction monitoring, AI agents can examine transaction workflows, identify inconsistencies across different formats, and resolve exceptions step by step with minimal human intervention.

In regulatory reporting, AI can gather data from different systems, generate the required reports, and track how each output was produced. These AI applications save time while supporting audit and compliance needs by being traceable and explainable.

Although such capabilities already exist, they are often manual, fragmented, and difficult to scale. Agentic AI enables financial organizations to move towards more automated, efficient, and scalable processes while maintaining the accuracy and transparency required in their highly regulated environment.

Building an Agentic AI Ecosystem

Launching agentic AI can be daunting, especially if other AI projects have failed internally. Mayzak's recommendation is to choose a manageable use case and let it grow over time. Success can build on success. While companies may aim to automate a 70-step business process, they find that they need to start somewhere. What works in the market is to tackle the problem step by step. Once you have made the first step work, you can then move on to the next, and so on.

Financial services organizations that stand out among their peers will be those that integrate agentic AI into a broader ecosystem that includes strong security controls, good data governance, and effective system performance management. As Mayzak states, doing this well will create an AI feedback loop, where leaders receive new signals from these systems to assess the effectiveness of their investments and generate reliable, actionable insights.

By iterating on pilot projects and continuously improving, companies will build agentic systems that can be measured, managed, and scaled. This will transform agentic AI into a sustainable competitive advantage.