Agentic AI is already showing real value in fintech when it's applied to approval workflows, not just front-end chat or customer support. One of the most effective uses we're seeing is AI agents working quietly in the background pushing approvals forward without relying on humans to chase documents, check details, or send reminders. In practice, an agentic AI can step in from the moment an application starts. It gathers and validates required documents, checks for completeness and consistency, applies basic risk rules in real time, and follows up automatically when something's missing — whether that's via voice, SMS, or email. It can also route applications to the right approver based on amount, risk profile, or internal policy, and escalate only true exceptions to humans with full context, not a pile of raw data. The real shift here is that the agent isn't just assisting the process. It owns the outcome moving an application to an approved or declined state as efficiently as possible. For fintechs, the impact is tangible: faster approval cycles, fewer drop-offs, and far less manual effort per application. Compliance improves as well, because every action, decision, and handoff is logged and auditable by default. From Dave King. https://www.linkedin.com/in/david-king-093136172/
Right now, the best way to implement AI in current workflows would be for retrieval-augmented generation (RAG) pipelines for research into company fundamentals. When looking into whether a particular trade makes sense, investors are going to likely ask similar questions across many of the companies being considered. Tracking down the sections in each document that could answer these questions can be time consuming. However, current LLMs still suffer from the hallucination problem. Because of this, the main differentiator will be the output quality of the LLMs and the training users have received in using agentic workflows and LLMs appropriately.
Hello, Let me share a quote from Dimitri Masin, Co-Founder and CEO at Gradient Labs: "We're moving beyond hyper-personalisation toward truly agentic AI — systems that don't just tailor experiences, but act on behalf of customers to resolve their needs autonomously. Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operational costs, but the market demonstrates it can happen sooner. AI systems will not just personalise customer experiences but autonomously act on behalf of users across inbound requests, proactive outreach, and back office operations — everything executing payments, resolving disputes, and managing compliance checks in real time. Intelligent agents manage entire customer journeys and compliance workflows end-to-end. The shift from "hyper-personalised" to "hands-on, proactive AI" will redefine what trust and efficiency mean in customer operations." Gradient Labs builds AI agents that automate call center voice, text, and email experiences with higher CSAT scores than most human teams. Please let me know if you need more information. Best regards, Antonina Ria PR Manager
Agentic AI, perhaps the latest buzzword in fintech, is moving from pilot to real-world applications, especially considering that almost 93% of financial institutions are confident in the ability of an autonomous AI to act without human input by 2027 (source: https://fintech.global/2025/07/25/93-of-firms-plan-to-adopt-agentic-ai-by-2027/). Agentic AI may, in the future, be used to automatically manage workflows related to compliance, fraud, credit decisions, and others. These are financial tasks where human latency is and will continue to be the biggest obstacle to processing speed. Finance departments would like to be able to use AI for tasks such as identifying and acting autonomously to manage and streamline efficiencies and enhance the accuracy of their assessments. AI is already capable of identifying and acting autonomously to manage and streamline efficiencies and enhance the accuracy of their assessments. AI is also already capable of identifying and adjusting to manage and control risks, and acting to protect without human intervention. The balance of control is the real challenge.
At CLDY, we tried using AI agents for banking work. The first time around, the customer support part was a disaster. We fixed the workflows and suddenly costs were down and customers were happy. These AIs now catch weird spending patterns that human teams miss. If you're going to do this, pick one real problem to solve first, then expand. Don't try to boil the ocean.
My team once launched automated cashback alerts. Shoppers were skeptical until they saw their savings appear in real-time. Then they couldn't get enough of it. That was just a simple AI. I think the next wave could actually handle someone's savings or budgeting for them, learning their habits. The trick is to make it dead simple and introduce it slowly so people get comfortable handing over their finances.
We found something interesting at Apps Plus. We built software agents that fix SaaS workflow slowdowns on their own. Once we handed off the boring, repetitive checks to the AI, the whole process sped up. For fintech, this kind of agentic AI works well for handling fast trades. If you need to grow without getting bogged down, it's an option worth considering.
The most realistic near-term use case for agentic AI in fintech is operational triage, not decision-making. Systems that can spot anomalies, prioritise cases, gather context, and hand a clear recommendation to a human will add value quickly without crossing risk lines. Agentic AI fits best in areas like customer service escalation, fraud review preparation, and compliance monitoring, where the work is repetitive but judgement still matters. The biggest risk appears when autonomy creeps into decisions that affect customer outcomes or regulatory exposure. That is where accountability can blur fast. Before agentic AI can be trusted at scale, ownership has to be explicit. Someone must always be responsible for the outcome. The most overhyped use case is fully autonomous credit or risk decisions. The trust gap is still too wide for that to be realistic.
AI financial advisors are about to get truly autonomous. They'll soon be rebalancing portfolios and executing trades for you, not just analyzing data. On StockCalculator.com, we've seen even basic AI help people make better ETF reallocation decisions when the market gets shaky. If you're thinking about trying this, start by automating the parts people find tricky. Feedback and accuracy improve fastest where users actually want to save time.