Agentic AI presents financial services with endless opportunities through its ability to analyse data instantly, but it’s not as simple as plug-and-play. As Himanshu Gupta, Senior Architect, Capital Markets, Solace, explains here, with five business use cases that demonstrate the importance of correctly integrated AI agents and how they deliver impact and ROI.
Reactive AI chatbots won’t cut it for financial organisations, every millisecond now counts! Enter, agentic AI agents capable of analysing and acting in real-time. For these agents to excel, they require a shared enterprise-wide view – they all need to be updated on other agents in real-time.
Agentic AI agents are taking capital markets by storm
By adopting this governed, real-time foundation, financial institutions are already unlocking high-value use cases that slash operational costs and drive revenue:
- New look portfolio management: Orchestrated AI agents are revolutionising portfolio management by processing trading signals across global markets to optimise execution and manage risk. By coordinating specialised agents, institutions can improve trade execution speed and reduce transaction costs.
- Detect & prevent fraud instantly: Financial institutions are deploying orchestrated AI agents to analyse daily transactions. Specialised agents coordinate insights on geographic inconsistencies, spending patterns, and merchant risk profiles to accurately distinguish threats from normal consumer behaviour.
- Meeting regulatory & reporting requirements: AI systems are automating global regulatory compliance to ensure accurate, timely submissions. Orchestrating dynamic workflows requires intelligent data routing and delivery to adapt to regulations and maintain synchronisation across filing schedules.
- Individual investment advice: Orchestrated AI agents are democratising financial advisory services by scaling personalised wealth management. By coordinating insights, platforms can adjust investment recommendations based on a client’s circumstances or market conditions.
- Smarter loan & credit systems: Agentic AI is transforming the manual loan approval experience by intelligently coordinating data verification across multiple internal systems and external agencies. To support these lending scenarios, dynamic orchestration infrastructure must route data efficiently while maintaining compliant audit trails.
What’s in it for Frontier Firms?
In a post for Microsoft, Thomas Shuster, Research Director, Worldwide Capital Markets, Wealth, and Digital Assets, IDC Financial Insights highlighted that some “frontier firms” are leading the way in agentic AI adoption, stating: “It is less about being first to experiment with new tools and more about translating AI investment into measurable, repeatable operating gains.”
He continues: “The step change occurs when firms shift from task acceleration to workflow redesign, deploying AI agents to execute multistep processes across systems under bounded human oversight.”
The six pillars paving the capital markets’ path to AI glory
Institutions that have successfully leveraged AI have not only developed sophisticated algorithms but also established the infrastructure and practices to orchestrate agents at scale:
Real-time results require real-time data streaming: Capital markets run on live data. Establishing a continuous, real-time, contextual stream of data ensures that market data, reference data, and trade events flow continuously to the AI systems.
Avoid Siloed-Agent Orchestration: Complex financial workflows are fragile. Systems must be designed where agents can discover each other, coordinate, and share context dynamically.
Embrace Open Standards: Building on open standards such as A2A or MCP and API-friendly protocols prevents vendor lock-in, allowing firms to swap out applications or cloud-native AI services with emerging technology without having to re-architect their systems.
Clear Governance: End-to-end autonomy in finance requires strict oversight. Establish clear policies for AI decision-making, data usage, and risk management before deployment.
People Investment: IT teams need to be able to design orchestration patterns, monitor agent performance, and adapt systems as business needs evolve.
Demonstrate Value: Begin with use cases that demonstrate clear ROI and require agent coordination, and use them to validate your orchestration approach, then scale using infrastructure that supports enterprise-wide deployment.
The key role of an Agent Mesh
As capital markets firms rush to leverage AI agents, many are investing without respecting how much orchestration and underlying connectivity matter. The bridge from agentic AI experimentation to full-scale production is an Agent Mesh platform that builds upon the scalability of an Event Mesh. By combining real-time, event-driven data distribution with a network of autonomous AI agents. While an Event Mesh routes data across the enterprise, the Agent Mesh introduces intelligent agents that autonomously reason about and act on live information flows.
The next AI chapter in financial services
The financial services industry has reached a critical juncture with Agentic AI. To capitalise on its potential, organisations must integrate AI agents across key operations.