Key Takeaways
- Financial services companies require a high-quality and accessible data store for agentic AI to succeed.
- Data readiness is crucial; it affects both the strengths and weaknesses of agentic AI systems in financial services.
- To deploy agentic AI effectively, financial firms need to ensure their data is well-indexed, secure, and managed at scale.
Financial services companies have unique needs when it comes to business AI. They operate in one of the most highly regulated sectors while responding to external events that are updated by the second. The success of agentic AI in financial services depends less on system sophistication and more on data quality, security, and accessibility.
“It all starts with the data,” says Steve Mayzak, global managing director of Search AI at Elastic. Agentic AI—systems that can independently plan and take actions to complete tasks, rather than simply generate responses—holds enormous potential for financial services due to its ability to incorporate real-time data and optimize complex workflows.
Gartner has found that more than half of financial services teams have already implemented or plan to implement agentic AI.
However, introducing autonomous AI into any organization magnifies both the strengths and weaknesses of the underlying data it uses. To deploy agentic AI with speed, confidence, and control, financial services companies must first be able to search, secure, and contextualize their data at scale. “Agentic AI amplifies the weakest link in the chain: data availability and quality,” says Mayzak.
Financial services companies require a trusted and centralized data store that is easy to access, dependable, and can be managed at scale. Regulation in the financial sector requires a high degree of accountability for all data tools. At the same time, financial services companies need speed and accuracy in order to meet customer expectations and stay ahead of competition.
The High Stakes of Quality Information
Regulation in the financial services sector requires a high degree of accountability for all data tools. As Mayzak says, “You can’t just stop at explaining where the data came from and what it was transformed into: ‘Here’s the data that went in, and this is what came out.’ You need an auditable and governable way to explain what information the model found and the logic of why that data was right for the next step.” That is, you need to be able to see, understand, and describe the underlying processes.
At the same time, financial services companies require speed and accuracy in order to meet customer expectations and stay ahead of competition. Markets are continually shifting, and risks and opportunities move along with them. If an AI model can parse natural language (unstructured data) from complex sources—in addition to structured data in spreadsheets that are easier to analyze—this gives users more relevant information.
In this environment, there is no tolerance for error, including the hallucinations that plagued early AI efforts. Agentic AI systems depend on rapid access to high-quality, well-governed data that is secure and accessible. In financial services, that data spans transactions, customer interactions, risk signals, policies, and historical context. The task of preparing that data for AI should not be underestimated. “Natural language is way more messy than structured data, and that makes the process of organizing and cleaning it up that much more important and also that much harder,” says Mayzak.
The data must be well indexed and consolidated across different locations, not locked in the silos of separate systems across the organization. Otherwise, AI agents lag, provide inconsistent answers, and produce decisions that are harder to trace and explain, undermining confidence among regulators, customers, and internal stakeholders.
Searching and Securing Results
An effective search platform is key to solving the problem of fragmented, poorly indexed, inaccessible data. Financial services companies that can readily sift through both their structured and unstructured data, keep it secure, and apply it in the right context will get the most value from agentic AI. This often requires designing AI systems with data access and utility in mind so they can work faster and yield more accurate results, as well as reduce risk.
“Search is the foundational technology that makes AI accurate and grounded in real data,” Mayzak says. “Search platforms have become the authoritative context and memory stores that will power this AI revolution.”
Once in place, these AI-enhanced searches and autonomous systems can serve financial services companies for a range of purposes. When monitoring client 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 trade monitoring, AI agents can review trade workflows, identify discrepancies across different formats, and resolve exceptions step by step with minimal human intervention. In regulatory reporting, AI can gather data from across systems, generate required reports, and track how each output was produced. These applications of AI save time while supporting audit and compliance needs by being traceable and explainable.
Building an Agentic AI Ecosystem
Leveraging the right tools is essential for financial services organizations that want to successfully launch agentic AI. Choosing a manageable use case and allowing it to grow over time can help companies navigate the challenges of implementing this technology. As Mayzak says, “Success can build on success.” By integrating agentic AI into a broader ecosystem that includes strong security controls, good data governance, and effective management of system performance, financial organizations will be better positioned to realize its full potential.
Doing this well will create an AI feedback loop where executives gain new signals from these systems to assess the effectiveness of their investments and generate reliable, actionable insights. By iterating on pilots and continuously improving, companies will build agentic systems that can be measured, managed, and scaled. This will transform agentic AI into a lasting competitive advantage.
Learn more about how Elastic supports financial services.
Originally published at technologyreview.com. Curated by AI Maestro.
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