How AI Agents Are Transforming Finance Teams

In a 2026 Cambridge survey of financial services firms, 52% of industry respondents were at least at the piloting stage with agentic AI. Yet only 14% said AI was already transforming their organization’s strategy and competitive advantage. Many finance teams now find themselves caught between trying an agent and changing how work actually gets done.

An agent that gathers the evidence for an invoice exception can save an analyst time. But an AI agent in finance that changes a vendor’s bank details or approves a payment raises new questions, such as who allowed it to act and how teams would catch a mistake.

What are AI agents in finance?

An AI agent is an autonomous system given a goal, access to information, and permission to use specified tools to complete a series of steps. It might read an incoming invoice, check the purchase order in an enterprise resource planning (ERP) system, compare the vendor’s details with the master record, request missing information, and prepare an exception for review. What makes it an agent is its ability to choose the next permitted step based on what it finds, rather than simply following a fixed sequence.

Agentic AI in finance can be narrow in scope and still needs strong controls: the more consequential its actions, the tighter those controls must be.

Agentic AI vs. traditional automation

Traditional automation follows preset rules. For example, if an invoice matches a purchase order within an agreed tolerance, it can be sent for payment automatically.

Agentic AI for finance is more flexible: it can interpret an unstructured message, decide which records to retrieve, use authorized tools, and adjust its approach if it detects a discrepancy.

Suppose a supplier asks why an invoice remains unpaid. An agent could compare the invoice, purchase order, and goods receipt, find a quantity discrepancy, seek clarification from the relevant employee, and prepare a reply. That investigative role would not, by itself, authorize the agent to change payment details or release funds.

Routine matches can remain with simpler automation, while agents handle less predictable cases.

Suggested read: AI Agents in Compliance: How MCP and Agent Skills Work

Data readiness: The foundation for agentic AI

An agent investigating an issue in finance, such as an unpaid invoice, needs to know which records it can rely on. The invoice might name a different legal entity from the supplier record in the ERP, or include bank details that conflict with those already verified.

For this workflow, data readiness means having usable invoices, purchase orders, goods receipts, supplier records, and approval limits, along with a clear way to resolve conflicts between them. AI may expose missing fields or duplicate records, but the people responsible for supplier data still decide whether a correction is valid. At Sumsub Finance Day, finance and risk transformation adviser Marc Temerlies said teams should not wait for perfect data. Instead, they should start with a use case where data quality can be measured and is good enough, then improve it through feedback.

A pilot could test the agent against routine invoices, known errors, and difficult exceptions. That would show whether it consults the right records and seeks help when they conflict, before any errors carry through into later workflows.

AI agents across core finance functions

AI agents can help when a task involves finding information across documents and systems, then deciding what needs attention. The sections below follow that cycle.

Vendor onboarding, invoicing, and validation

A new supplier may send registration forms, tax documents, bank details, and a first invoice through different channels. An agent could assemble the files, compare the legal name and other details across documents, and identify what is missing before the supplier record is approved. AI invoice processing can then use that record to check whether the first invoice comes from the expected entity.

The agent might notice that the invoicing entity differs from the contracted entity or that an invoice number appears twice. It could then bring the conflicting documents together for a human reviewer.

Accounts payable (AP) workflow and approval automation

Once a supplier is active, accounts payable automation can route invoices that meet established matching rules. AI AP automation adds value when an exception requires context. For example, if an invoice exceeds its purchase order by 8%, an agent could look for a contract amendment, compare the charges, and show a buyer why the amount differs.

That explanation may resolve the discrepancy or show that approval is needed. It does not give the agent authority to release the payment. Preparation and approval can remain separate steps.

In a client case study, Deloitte reports that its AI invoice-management solution achieved 92% touchless processing and reduced average invoice-processing time by 50–75%. The agents were trained on the client’s invoice intake process, controls, and policies, and could interpret nonstandard invoices and recommend how to complete missing fields.

Accounts receivable (AR): Cash application and collections

On the accounts receivable side, money arriving from customers creates a different matching problem. A single payment may cover several invoices without listing every reference clearly. An agent could compare the remittance email, open invoices, amounts, and payment history, then propose an allocation with evidence for each match. This lets AR teams handle cases that a reference-number rule leaves unresolved.

The same records can inform collections. If an overdue balance is tied to a billing dispute, the agent could identify the dispute and draft a follow-up that reflects it. Incorrect allocations and payment reversals would need to be tracked alongside any improvement in posting speed.

Accruals and journal entry automation

Costs can build up before the invoices for them arrive. AI agents in accounting could identify services received but not yet billed, gather the contract and usage records, and prepare an accrual calculation under an approved policy.

The proposed journal entry would need to show the amount, period, accounts affected, and expected reversal. An accountant can then assess the supporting evidence and check whether a late invoice has already been recorded for the same expense.

Vendor performance, spend, and tax documentation

An agent can join purchase orders, invoices, service records, and contracts to identify late deliveries, price drift, or spend outside agreed terms. Vendor management AI can see patterns a buyer might miss across several departments, then assemble the underlying transactions before anyone raises the issue with a supplier.

The agent could also reveal missing or expired tax forms. Whether a form is valid or a particular tax treatment applies remains a decision for staff with the relevant expertise.

Financial planning and analysis (FP&A) and reporting

Once transactions have been recorded, AI financial analysis can help explain what changed. An agent could trace a regional revenue shortfall from a management report back to sales volumes, prices, exchange rates, and any reclassifications, then draft commentary that identifies which factors the records support.

It could also produce scenarios from assumptions supplied by the FP&A team. Analysts remain responsible for challenging those assumptions and deciding which explanation or forecast belongs in management reporting.

Financial close, consolidation, and disclosure

During the close, agents can track outstanding reconciliations, compare intercompany balances, and alert owners to differences before sign-off. Each proposed adjustment would need to link back to the figures and records supporting it.

An agent might prepare a consolidation adjustment or draft disclosure commentary from approved numbers. Version histories and separate approval of entries would let reviewers see what changed and who accepted it.

In February 2026, Goldman Sachs chief information officer Marco Argenti told CNBC that the bank had spent six months working with embedded Anthropic engineers to develop Claude-based agents for data-heavy back-office work, including trade and transaction accounting and reconciliation, as well as client due diligence and onboarding.

Risk, fraud, and compliance monitoring

For AI agents in banking, the use cases extend from back-office finance to customer onboarding, transaction monitoring, and investigations. An agent can assemble a case history, compare activity with established patterns, and prepare a documented alert for an analyst.

Banks expect adoption to spread. In Accenture’s 2026 banking trends report, 57% of banking IT executives expected broad or fully embedded adoption of AI agents within three years in risk, compliance, audit, fraud detection, and transaction monitoring. And 56% expected that level of adoption in credit assessment and loan processing.

AI risk management in finance must account for both missed threats and unnecessary restrictions on legitimate customers. Teams must define what the agent can prioritize, what it must escalate, and who makes the final call. The agent can organize the investigation, but the bank remains responsible for deciding whether to file a suspicious activity report. 

Suggested read: How Does Verification of Payee Work?

Governance, control, and human oversight

AI governance in financial services begins with ownership. Each workflow needs an owner who decides what the agent may access, which actions it may take, and what happens when the evidence is insufficient. The cost of a mistake determines how much review an action needs.

Governed by design: Audit trails and controls

An AI audit trail must let a reviewer reconstruct an action, not just read the agent’s conversation. For an invoice, that means linking the records consulted, the model and policy versions, the proposed decision, the approvals, and the final changes. If a payment is questioned later, the team can see which information the agent used and who authorized the result.

The audit trail is only one part of control. An agent might flag a request to change a supplier’s bank details, but the system itself should block the change until independent verification and approval are complete. Confirming the new details with the supplier should be a required step, not left to the agent’s discretion.

Human oversight and accountability in agentic AI

Human-in-the-loop AI works only if reviewers can assess what they are shown and have the time to do so. A confidence score is of little use on its own. Reviewers need the source records, the proposed action, and any conflict with policy, along with the authority to reject the recommendation and record their reasons.

A routine invoice that matches verified records may follow an approved automatic route. Changed bank details or a material journal entry call for closer review, because an error would be harder to undo. Sampling cases that cleared automatically helps catch mistakes that would otherwise never reach a person.

Human oversight also creates feedback. Investigators and accountants should be able to correct a case, explain why, and add the outcome to future tests. The agent itself should never be able to change its own controls.

Data privacy, security, and regulatory compliance

Governed AI workflows limit what an agent can see and what it can do with that information. An invoice or supplier email can provide facts for a case, but it may also contain hidden instructions meant to redirect the agent – a technique known as prompt injection. Such instructions must never give the agent authority to access another system, export data, or bypass an approval.

Requirements vary by activity and jurisdiction. In the EU, the European Banking Authority and its fellow supervisory authorities have called for stronger governance and risk management to address cyber risks from frontier AI models in the financial sector. In the US, NIST’s AI Risk Management Framework offers a voluntary structure for managing AI risk. Before deployment, a finance team should map its use case to the applicable privacy, recordkeeping, financial reporting, outsourcing, and sector-specific obligations.

Suggested read: From AI Agents to Know Your Agent: Why KYA Is Critical for Secure Autonomous AI

Measuring the ROI of AI agents in finance

Faster processing is easy to observe; financial return is not. In the Cambridge Centre for Alternative Finance’s 2026 survey, 55% of industry respondents said they found it difficult to measure the value of AI deployment, rising to 76% among large financial institutions. For finance agents, measurement should begin with the work they are meant to improve: how long cases currently take, how often they need reworking, and what errors cost. A pilot can then be measured against that baseline and, where possible, against a comparable group handled through the existing process.

Case studies show where operational gains can become financial value. Genpact reports that a global supply-chain distributor cut invoice-processing cycles from 18–29 days to 9–14 days after an AP transformation using its agentic AI suite. Faster processing meant more invoices were paid within early-payment windows, and discounts captured rose from $35 million to $44 million.

Time savings alone are often smaller. Suppose a team processes 24,000 invoices a year and an agent cuts handling time by two minutes for half of them. That releases 400 hours annually (24,000 × 50% × 2 ÷ 60), or $12,000 of staff capacity at an assumed $30 per hour. This becomes a cash saving only if costs actually go down – through reduced overtime, contractors, or hiring. Otherwise, the value depends on what staff does with the time, such as clearing a backlog.

The net benefit must also account for software, integration, testing, human review, and support costs, along with any losses caused by new errors. Costs can be reduced by splitting an agent’s work into smaller tasks and using the least costly model that passes the required evaluations for each.

The relevant outcomes differ by workflow. An invoice pilot might track resolution time, cases completed within policy, rework, incorrect payments, and reviewer overrides. Accounts receivable teams may care about unapplied cash and disputed payments, while a fraud team needs to count missed cases as well as false positives. A faster process has little value if its mistakes become more expensive.

How to choose and deploy your first use case

Once a team has reliable records for a defined workflow, the next question is where an agent could improve on existing automation. The first use case for AI agents for accounting or any other finance team should have a real bottleneck, available evidence, and a clear boundary around actions. Teams should start with the operational pain and understand how a failure would be detected, contained, and corrected before launch.

A practical rollout has six stages:

1. Choose one workflow. Map its volume, current costs, delays, and failure points; decide whether rules-based automation would solve the problem on its own.
2. Define the boundary. Specify source systems, permitted reads and writes, escalation triggers, approval authority, and a named process owner.
3. Build an evaluation set. Include routine cases, known past mistakes, ambiguous documents, and adversarial inputs. Check the correctness of actions and evidence, not merely the fluency of answers.
4. Run in shadow mode. Let the agent propose outcomes while the existing process remains authoritative. Compare both decisions, investigate disagreements, and establish a baseline for errors.
5. Release limited actions. Begin with suggestions or reversible, low-risk tasks. Allow broader actions only when the controls and observed results justify them.
6. Monitor and revisit. Sample approved and rejected cases, watch for changing error patterns, retest after model or policy updates, and make it possible to pause the workflow quickly.

Gartner predicts that more than 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear value, and inadequate risk controls. Forecast or not, the lesson for a first deployment is to show what the workflow improves and how the organization will handle the cases it gets wrong.

At Sumsub Finance Day, Eugeny Malyutin, Head of LLM at Sumsub, put the likely failure rate nearer 60%, adding that because zero risk is impossible with probabilistic models, evaluation is roughly 90% of the work.

AI agents in finance by company size

For a small finance team, a packaged tool that works with its existing accounting system may be enough for narrow tasks, as long as it saves time and staff can still review the records behind each decision and keep control of approvals.

Mid-sized organizations with several entities may first need consistent supplier identifiers and approval rules across systems. Large institutions have more specialist resources, but also more integrations and access boundaries. A central register of deployed agents shows where each one operates, while each process owner remains responsible for the agent’s permissions and results.

Common pitfalls deploying AI agents in finance

AI does not fix a poorly designed workflow. If ownership, thresholds, escalation criteria, or source data are unclear, an agent may simply run the same flawed process faster. Nor is an agent always the right tool: where a process has clear inputs, rules, and outputs, deterministic automation is usually cheaper and more predictable.

Once an agent is live, a polished answer can hide a wrong calculation or a decision based on the wrong record, and broad write access raises the stakes when it meets an unusual case or a misleading document.

Performance can also drift after a successful pilot. A new supplier format, model update, or policy change may alter which cases the agent handles well, so ongoing review of errors, rework, and costly misses should decide whether it keeps its current level of authority.

FAQ: AI agents in finance

• What are AI agents in finance?

  Agentic AI in finance refers to systems that pursue a defined task by retrieving information, choosing among permitted steps, and using tools within set boundaries. For example, an agent might investigate an invoice discrepancy and prepare an action for approval.

• How are AI agents different from traditional automation?

  Traditional automation follows a predefined path or set of rules for each situation, while an agent can choose its next permitted step based on what it finds. Fixed rules still govern approvals, limits, and other decisions that must be enforced predictably.

• Where can AI agents be used in finance?

  AI agents can support accounts payable, accounts receivable, forecasting, the financial close, vendor checks, and risk investigations by collecting evidence and preparing exceptions for review. AI agents in accounting can also draft reconciliations or journal entries for an authorized reviewer.

• How does human-in-the-loop work in finance?

  Human-in-the-loop AI routes specified actions and uncertain cases to someone with the authority and evidence to decide. The surrounding system should enforce that review before a payment or other consequential action goes ahead.

• Can AI agents integrate with ERP systems?

  Yes. Where an ERP system offers suitable APIs or approved connectors, an agent can retrieve records and carry out specifically authorized actions. The integration should preserve the ERP’s permissions, validation rules, and change history.

• Are AI agents secure and compliant for finance?

  AI agents can be deployed within a controlled process, but security and compliance depend on the use case, jurisdiction, data handling, permissions, and testing. An AI audit trail in finance should link the agent’s inputs, actions, and approvals to the final accounting record.