As the fintech sector hits growth levels reckoned to be three times that of the global finance sector as a whole, fintech enterprises are looking to consolidate sustainable, cost-efficient growth. In particular, they are doubling down on achieving faster, more reliable core functions such as customer support and internal request management.
Despite predictions that automation advances would transform these areas’ efficiency, this step change in process improvement has proven elusive until the emergence of agentic AI ─ driving process overhauls and raising the prospect of autonomous operations that can understand the logic of highly complex processes and then improve them in the way the human mind does.
Agents: rethinking processes and supporting compliance
Unlike limited or static automation approaches, AI agents can reason, act, and adapt across the tech stack’s data sets, applications, and cloud environments, independently of large language models (LLMs) or vendor constraints. This ensures that agents operate safely, ethically, and in compliance with the raft of internal policies and finance sector regulations including processes for handling customer information.
Based on innovations such as new composable agent foundations, fintechs are starting to build their own dynamic, adaptive AI agents, improve automation processes, and set up pathways towards autonomous agent capabilities. IT teams use agents to bridge ‘gaps’ where single-purpose SaaS app agents become stuck or come into conflict with each other, and end up absorbing excessive IT resources.
Harnessing these platforms, AI agents evolve from task-runners to digital teammates capable of pursuing goals based on context, tools, and both structured and unstructured data. This breakthrough unlocks the road to autonomy at scale, enabling IT leaders to move from static automations to deploying horizontal agents that can reason, act, and adapt across multiple systems and processes. This shift also enables fintechs to further transform processes, including improving data handling and addressing such as unplanned manual interventions by IT and line of business teams.
Realising agents’ potential
Agents’ potential in fintech is game-changing. Analyst Globant predicts that fintech AI agents’ global value will increase from $2 billion in 2024 to $80 billion in 2034. A UK trade body has already noted financial institutions’ trials of multiple AI agents working together to break down common problems into smaller tasks to meet objectives.
Despite these expectations, fintechs have encountered agent integration challenges, particularly achieving real-time, bidirectional data flow and unified process orchestration across multiple tools and often siloed vendor ecosystems. Forward-looking companies have started to address and resolve these issues by harnessing pre-built AI agents and dynamic integration capabilities from a composable AI agent platform.
Expense specialist rethinks intake to cut manual interventions
In a recent case, an expense management provider overhauled its credit-limit request intake by consolidating fragmented Slack-based forms, validation, and Zendesk ticketing into one AI agent, eliminating intermittent sync failures and simplifying request routing, reducing manual follow-up and operator confusion.
Previously, the company’s operations teams had to cope with fragmented intake processes and workflows and siloed departmental environments; cross-team communications were stitched together with Avenue and Slack but this setup remained brittle. The key ongoing issue was that Zapier integrations frequently broke down and the internal triage and communications systems lacked the flexibility, validation, or governance controls to enable true coordination across Slack and Zendesk.
To resolve this issue, the IT team deployed a new type of composable platform to consolidate workflows’ multiple layers ─ including intake, validation, ticketing, and notification ─ into a single agent-driven process. Slack forms can now dynamically validate inputs by querying internal data, Zendesk tickets are auto-created with complete data fidelity, errors and typos are caught, and system routing rules aligned to business hours or escalation urgency.
Teams can now build logic into the agent for Slack, Salesforce and other proprietary tools, enabling execution across them. The company is also trialling AI agents that handle inquiry triage, answering simple questions, summarizing ticket threads, and drafting responses, layered on top of the operational flows.
The company now has a foundation for its agent development, with IT already developing an extensible toolkit enabling each automation to become a tool that an agent can invoke. These tests show agents acting autonomously can summarize complex Slack threads and ticket histories and escalate issues based on business rules. This shift to dynamic agents is cutting manual interventions and unlocking time savings across different support teams.
AI agents resolve service request overload issues
Agents can address and resolve fintechs’ manual intervention issues found in cross-functional operations. A subscription management provider set out to transform employee support across departments including IT, HR, finance, procurement, and sales.
Integration issues included disjointed systems and a support model reliant on manual fulfilment, disconnected first-generation bots, and workflow automation that couldn’t scale. With more than 200 SaaS applications in use, each offering its own AI-powered feature, the employee experience was clunky and inconsistent. IT support teams were overwhelmed with service requests and couldn’t scale responses efficiently.
The company has deployed a unified AI agent that lives in Slack and provides a single point of contact for departments’ needs. Connected to multiple systems via the company’s AI-ready iPaaS, the agent uses a single interface to surface and orchestrate behind-the-scenes workflows. It abstracts previously fragmented tooling into one user experience to reduce reliance on vendor-specific bots.
Measured results revealed that the IT support agent has reduced support tickets by 75%, freeing up L1 and L2 support teams to work on strategic initiatives. The company CIO has underlined that agent adoption demands the alignment of tech innovations with core business goals to drive measurable outcomes. The new platform has given their team the agility and governance to experiment and scale without sacrificing control.
Towards transformation
Fintech companies have made breakthroughs using a composable AI agent platform and are paving the way to wider process improvements, business transformation and ultimately, the autonomous enterprise.
