AI In Finance: How Autonomous Agents Are Changing Fintech

The conversation around artificial intelligence in finance has, for the past two years, been dominated by generative AI—chatbots providing stock tips, co-pilots summarizing earnings reports, and assistants drafting emails. But this is just the opening act. A far more profound, structural shift is underway, moving from AI as an "assistant" to AI as an "actor."

Welcome to the era of autonomous agents.

These are not just tools that suggest or predict; they are sophisticated AI systems designed to execute complex, end-to-end financial workflows with minimal human intervention. While the industry remains hesitant to let an algorithm "approve a billion-dollar transaction," according to industry experts, these agents are already being deployed to automate the core plumbing of the financial world.

This isn't a future-tense revolution. According to a recent report from Capgemini, while over 87% of financial firms use traditional AI and 32% use generative AI, only 10% are currently using AI agents at scale. This gap represents the next great frontier in financial technology, a silent arms race to build autonomous systems that can manage risk, move money, and close the books.

The Shift from "Assistance" to "Execution"

To understand this change, it's crucial to distinguish between today's popular AI and true autonomy.

An AI "assistant" or "co-pilot," like a ChatGPT-powered bot, is a reactive tool. A human is firmly in the loop. You ask it to summarize a report, it provides a summary. You ask it to draft a fraud alert, it provides a draft. The human must verify the work and then execute the final action.

An autonomous agent is a proactive system. It is given a goal, not just a task.

"The idea that you can take a pile of documents and feed them to an LLM and get a cohesive response that you can rely on is really a fallacy," said Adam Dell, CEO of Domain Money, at a recent Benzinga panel on the topic.

Instead, the agentic approach, as described by Armando Benitez, head of AI at BMO Capital Markets, is modular: "one entity that does one thing and one thing only really well. And then when you put them all together you can solve a really complex problem."

This "multi-agent" approach is where the power lies. An agent's goal isn't just to "flag a weird transaction"; its goal is to "ensure the integrity of the payment network." This goal might involve:

1. A "Watcher" Agent: Scans millions of transactions in real-time.

2. An "Investigator" Agent: When the Watcher flags an anomaly, this agent autonomously gathers context—checking the user's location, device history, and past transaction patterns.

3. A "Decision" Agent: Based on the investigation, this agent scores the risk and acts. It might autonomously freeze the account, trigger a multi-factor authentication request, or clear the transaction, all before a human analyst is even aware of the event.

The New Workforce: Where Agents Are Deployed Today

While the C-suite is still far from handing over the keys to the treasury, autonomous agents are already proving their value in high-volume, data-heavy, and rule-based functions.

1. Revolutionizing Fraud Detection and Risk

This is the most mature beachhead for autonomous AI. The speed of digital fraud has long outpaced human capabilities, making it a perfect job for agents.

In this "agentic era," systems go beyond static rules. They actively hunt for and adapt to new fraud patterns. According to experts at Mastercard and other major financial institutions, AI agents are now being deployed to:

• Prevent Fraud in Real-Time: By analyzing transaction patterns, location, and user behavior before a transaction is approved, agents can stop fraud at the point of sale, not just report it afterward.

• Reduce False Positives: A major drain on resources, false positives (legitimate transactions declined as fraud) frustrate customers and lose revenue. Agents use deeper contextual understanding to approve transactions a human analyst might have flagged, improving the customer experience.

• Manage Credit Risk: In lending, agents are moving beyond static credit scores. They can create a dynamic risk assessment by analyzing real-time cash flow, market conditions, and spending habits, offering a far more accurate picture of creditworthiness for both individuals and businesses.

2. Automating the Unseen World of Corporate Finance

For many, "FinTech" conjures images of trading apps and digital payments. But one of the biggest applications of autonomous agents is in the unglamorous, critical backend of corporate accounting.

Companies like Nominal.so are building agents to automate entire accounting workflows, moving finance teams from "doing the work" to "governing the outcomes." Key use cases include:

• The Autonomous Month-End Close: Instead of a 10-day scramble of manual data entry and reconciliation, agents work continuously. They validate transactions as they happen, prepare consolidations across multiple entities and currencies, and generate close documentation in real-time.

• Continuous Reconciliation: An agent can be tasked with "ensuring the bank ledger matches the general ledger." It will run 24/7, matching transactions, identifying the cause of exceptions, posting its own correcting entries, and documenting its results for auditors.

• Intelligent Underwriting: In insurance and lending, agents can fully automate the data-gathering pipeline—pulling applicant data, verifying income from bank APIs, checking compliance databases, and presenting a complete, decision-ready file to a human underwriter.

3. The (Cautious) Future of Autonomous Trading

For decades, algorithmic trading has used "bots" to execute pre-programmed, rule-based strategies. Autonomous agents are the next evolution.

An agent's goal is not "if X happens, sell Y." An agent's goal is "manage this $10 million portfolio within these risk parameters." To do this, it can:

• Generate its own strategies based on market-moving news.

• Scan alternative data like social media sentiment or satellite imagery.

• Execute trades and then learn from the results to modify its own strategy, all without direct human input.

This is also where the industry's anxiety is highest. The 2012 "Flash Crash" caused by a single rogue algorithm is seared into the market's memory. Full autonomy in trading remains the final, and most terrifying, frontier.

The "Human in the Loop" Is Not Going Away

Despite the "autonomous" label, the financial industry is unanimous on one point: the human is not optional. The current state-of-play is what Mitchel Jones, CEO of Lava, calls the "AAA" framework: Assist, Advise, and Approve.

"Most fintech companies are still in the 'assist' zone," Jones said. "They use AI agents to speed up and enhance context. But, they're still a long way from letting agents approve billion-dollar transactions or make decisions to offset fraud."

The "human-in-the-loop" (HITL) model, or "human-on-the-loop" (HOTL), remains the gold standard. Agents are being trusted to execute 99% of a workflow, but they must escalate high-risk, high-value, or ambiguous decisions to a human supervisor for the final "approve" step.

This is a matter of trust, regulation, and security. The recent, first-ever documented case of a state-sponsored group using AI agents to orchestrate a cyber-espionage campaign shows the dual-use nature of this technology. If an AI agent can autonomously find and exploit a software vulnerability, it can certainly find and exploit a market-data or security-protocol vulnerability.

The Future: A Bank Run by a "Multi-Agent Ecosystem"

The end-goal for FinTech is a "multi-agent ecosystem" where specialized agents collaborate like a human team.

A customer's request—"I want to get a mortgage"—could trigger a cascade of autonomous activity:

• An "Onboarding Agent" interacts with the customer to gather initial documents.

• It passes the file to a "Credit Agent" that pulls credit, analyzes income, and assesses risk.

• A "Compliance Agent" simultaneously runs KYC (Know Your Customer) and AML (Anti-Money Laundering) checks.

• Finally, an "Underwriting Agent" assembles all the data, approves the loan, and generates the final documents for the human to sign.

This future isn't a decade away. The components are being built and tested in silos right now. The firms that can safely and effectively bridge those silos—moving from "assist" to "approve"—will be the ones who dominate the next era of finance. They will operate at a speed, scale, and cost-efficiency that their human-centric competitors simply cannot match.