The fintech industry is moving beyond simple chatbots.
As companies face the challenges of handling vast amounts of data and stricter regulations, the emphasis has moved towards “agentic workflows.
Such autonomous systems are designed not just to process information but also to take logical next steps in decision-making.
Streamlining investment basics
For many companies, the first major challenge is data retrieval. According to Colby Mainard, Machine Learning Engineer at MVP, the most effective immediate use of technology is in “retrieval-augmented generation (RAG) pipelines” for researching company fundamentals.
When evaluating potential trades, investors often ask the same questions about different entities. Mainard points out that locating the relevant sections in each document to answer these questions can be time-consuming.
By automating this search process, analysts can avoid the tedious task of manually reviewing documents.
Addressing the compliance conundrum
A significant issue for fintech companies is responding to regulatory inquiries.
Zahra Timsah, CEO and co-founder of i-GENTIC AI, describes the typical internal response as a “familiar scramble,” in which several departments—from Fraud to Legal—rush to piece together information under tight deadlines.
Timsah asserts that “in the very near future,” the fintech industry will realise the power of agentic AI in managing compliance.
By converting regulations into code, these systems can control data movement with a precision that is hard for humans to maintain during high-pressure audits.
Completing the automation cycle
The true benefit of these systems is their ability to complete tasks.
Justin Belmont, Founder and CEO of Prose, suggests that the most practical applications of this technology “aren’t flashy robo-advisors, it’s decision automation that actually completes processes.”
Belmont envisions agents that can “monitor transactions, identify anomalies, request missing information, and update risk models” without needing constant human oversight.
The aim is to eliminate bottlenecks “anywhere work dies in handoffs,” transforming AI from a novelty into a foundational part of infrastructure.
Managing the risk of errors
Despite the optimism, this technology does have its shortcomings.
Mainard warns that “current large language models (LLMs) still suffer from generating false information.” Because of this, human oversight remains crucial.
Mainard believes that the “main differentiator” in the market will be not just the technology itself but “the training users have received in using agentic workflows and LLMs appropriately.”
Ultimately, the effectiveness of the tool depends on the professional’s skill.
Enforcing policy in real-time
One significant benefit of a “governed agentic workflow” is its ability to enforce company policy instantly.
Timsah highlights that when data moves quickly, “nobody can confidently say who accessed what and where it went.” An agentic system, however, can:
- Limit data exports to only what’s strictly relevant to an inquiry.
- Automatically redact sensitive information.
- Route exceptions for immediate approval.
- “Capture a complete audit record of who requested what and where it went.”
The Value of ‘boring’ AI
While the tech industry often chases after the spectacular, Belmont argues that the most successful fintech agents will be “narrow, auditable, and boring in a good way.”
The industry shift will be complete when teams stop questioning whether AI can perform a task and instead ask, “Why is a human still doing this?”
Timsah agrees, noting that the “practical result is a response package that is easier to defend, delivered faster, with fewer remediation tasks.”
By concentrating on these critical yet “boring” administrative tasks, agentic AI is set to become the new backbone of global finance.