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AI Agents in Finance: Use Cases, Benefits, and Risks
Woosung Chun
CFO
Woosung Chun
CFO
Woosung Chun is the CFO of DualEntry with experience in corporate finance, accounting, strategy, and acquisitions. He previously grew from scratch and led the M&A and Finance teams at Benitago, where he completed more than 12 acquisitions in 2 years. He graduated with a BS from NYU Stern. At DualEntry, Woosung writes about AI in accounting, revenue recognition, foreign currency accounting, hedge accounting, and ERP modernization for finance teams navigating complex, multi-entity environments.
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Last updated
October 12, 2026
Reviewed by
Do San (Justin) Myung
Do San (Justin) Myung
Expert Accountant & Former Consulting CFO
Justin (Do San Myung) is Expert Accountant at DualEntry with 20+ years of hands-on experience managing general ledgers, financial close processes, and ERP implementations for mid-market and enterprise companies. As a former Consulting CFO and Controller, he has personally overseen month-end closes, SOX compliance programs, and multi-entity consolidations across technology, manufacturing, and services industries. Justin specializes in transforming manual accounting workflows into automated, AI-driven processes.
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Summarize this article
AI agents in finance are software systems that can work through multi-step financial tasks, make decisions based on the information they find, and take actions within the permissions they are given. They can support work across accounting, accounts payable, accounts receivable, forecasting, treasury, and the month-end close.
For finance teams, that means more of the work can move without someone manually handling every step. But the level of autonomy that makes sense depends on the task, the judgment involved, and the controls around it.
What are AI agents in finance?
AI agents in finance are systems designed to complete a goal rather than wait for a separate instruction at every step. They can gather information, decide what to do next, use other tools or systems, and take actions within the permissions they have been given.
That could mean investigating an unreconciled transaction, processing an invoice, following up on an overdue payment, updating a forecast, or keeping track of what is still outstanding during the close.
Agentic AI vs. generative AI in finance
The two terms are sometimes used interchangeably, but they do different jobs.
Generative AI produces something you then use, such as an answer, summary, analysis, or draft. Agentic AI goes further by working toward a goal across several steps and taking actions along the way.
A simple way to think about it is that an agent gives generative AI something closer to hands. The model can still read, interpret, and generate information, but it can also use systems and tools to carry the work further.
That does not mean every finance workflow needs an agent. If the path is predictable and the same rule should apply every time, traditional automation may still be the better fit. Agents become more useful when the work changes depending on what the system finds.
How AI agents work in a finance team
Say an invoice comes in and does not quite match the purchase order. A normal workflow might flag the mismatch and send it to AP.
An agent could go further by checking the invoice, purchase order, receiving record, vendor history, and other available information to work out where the difference came from. If it can resolve the issue within the rules it has been given, the workflow can continue. If it cannot, AP gets the exception together with the information the agent has already gathered.
The same pattern applies elsewhere in finance: give the agent a task, let it gather the information it needs, allow it to take certain actions, and send the work back to a person when it reaches something that needs judgment.
12 AI agent use cases in finance
AI agents are useful across several parts of finance, but some workflows are better suited to them than others. The strongest candidates tend to happen frequently, rely on information the agent can actually access, and have a clear point where a person can step in if something unusual comes up.
Here are 12 areas where that can work in practice:
1. Reconciliation and exception handling
A lot of reconciliation work can already be automated using matching rules. Transactions with the same amount, date, reference, or other clear identifiers do not need an AI agent to figure out what happened.
The harder part is everything left over. An agent can compare the general ledger with the bank statement or subledger, review transaction details, pull supporting records, and investigate whether a difference comes from timing, a missing entry, a duplicate, or something else.
That gives the accountant a much better starting point. Instead of receiving a list of unexplained mismatches, they can review the exception together with the information the agent has already found.
2. Month-end close coordination
The month-end close has plenty of work that still needs an accountant's judgment, but it also involves a lot of chasing. Someone needs to know which reconciliations are complete, which journal entries still need support, which approvals are outstanding, and whether one unfinished task is blocking something else. On a larger team, simply keeping the close tracker accurate can become work in itself. And the window is short: APQC's open-standards benchmark puts the median cycle time to complete monthly consolidated financial statements at 6.0 calendar days across more than 11,000 companies [1].
An agent can keep more of that coordination moving by monitoring open work, following up on overdue items, surfacing blockers, and updating tasks as the underlying accounting work changes. The accountant still owns the decisions that require judgment, but less time goes into figuring out what is outstanding and who needs to do something about it.
3. Invoice processing and coding
Invoice processing involves much more than reading the amount and due date from a PDF.
The system also needs to identify the vendor and entity, determine what was purchased, choose the right accounting treatment, check whether the invoice has appeared before, and decide where it goes for approval.
An agent can move through more of that process using information from the invoice, vendor record, purchase order, historical coding, and company workflow. When something looks wrong or falls outside its authority, it can send the bill to AP with the issue already identified.
4. Three-way matching and AP exceptions
Three-way matching is easy when the invoice, purchase order, and receiving record all agree. The work begins when they do not. Maybe the quantity is different. Maybe the vendor changed the price. Maybe freight or tax explains the gap, or the goods were received but nobody recorded the receipt.
An agent can compare the records and investigate where the difference comes from before somebody in AP has to step in. Smaller discrepancies may be handled within a tolerance the company has already approved, while unusual cases can be routed for review with the relevant documents attached.
5. Cash application
Cash application gets complicated when a payment does not line up neatly with one invoice. A customer might pay several invoices at once, send a partial payment, use a different reference number, or send the remittance information separately. Someone then has to work out what the payment belongs to before AR can clear it.
An agent can compare the payment with open invoices, customer history, balances, and remittance information to identify the most likely match. Straightforward cases can keep moving, while unclear payments and disputes go to the AR team.
6. Collections and payment follow-up
A lot of collections work is repetitive before it becomes complicated. An agent can monitor aging, identify invoices as they become overdue, send routine follow-ups, record responses, and track promises to pay.
If a customer pays, disputes the invoice, or stops responding, the next action can change accordingly. That leaves the AR team with more time for situations where the customer relationship or payment arrangement actually needs a person.
7. Forecast updates
Forecasts get stale because keeping them current takes work. Someone has to bring in the latest actuals, update the relevant drivers, understand where performance has moved away from plan, and decide whether the assumptions for the rest of the period still make sense.
An agent can do more of that preparation by pulling in the latest results, identifying which drivers changed, and preparing an updated view for the FP&A team. The analyst still decides whether the assumptions are sensible, but less time goes into rebuilding the model before analysis can begin.
8. Variance analysis and management reporting
A variance report tells you what changed. Finance still needs to work out why it changed. An agent can compare actuals with budget, forecast, or prior periods, identify the accounts or business units driving the movement, and pull together the underlying transactions. That gives FP&A a starting explanation instead of a blank page and a spreadsheet full of numbers.
The same workflow can help with recurring management reporting. Finance can begin with the analysis and schedules already assembled, then spend more time challenging the explanation and adding business context.
9. Cash positioning and liquidity monitoring
Treasury teams often need to bring together balances from different banks, entities, and currencies before they can work out how much cash is actually available.
An agent can gather that information, incorporate expected inflows and outflows, and keep the company's cash position current as balances change. That makes it easier to spot a potential shortfall, excess cash, or unexpected movement before the next scheduled report.
10. Finance policy and workflow checks
Not every useful finance agent needs to post an entry or move money. Agents can also monitor whether work is following the rules the company has already set. They might check whether an expense has enough documentation, whether an approval is missing, whether something looks unusual for that employee or department, or whether a workflow has stopped moving.
This is where an agent can be more useful than one fixed threshold. A transaction might look unusual because of the merchant, employee role, documentation, and surrounding activity together rather than because it crosses one dollar amount. The ACFE's Occupational Fraud 2026 report, which examined 2,402 cases, found that organizations with proactive data monitoring and analysis in place had 53% lower median losses and detected frauds 44% faster, with management review showing a similar effect (55% and 44%) [2].
11. Journal entry preparation
Recurring journal entries often involve pulling together the same information each period before anyone can actually review the accounting. That might include calculating an accrual, checking prior-period entries, gathering support, or reversing an entry from the previous month.
An agent can collect that information, prepare the entry, and attach the supporting detail for review. The accountant still decides whether the treatment is appropriate and whether the entry should be posted, but less time goes into assembling everything needed to make that decision.
12. Audit support and document retrieval
Audit requests can turn into a lot of searching. Someone has to find the transaction, pull the invoice or contract, check the approval history, and make sure the support matches what was recorded in the books.
An agent can gather that evidence across the systems it has access to and prepare it for review before it goes to the auditor. It can also help finance teams answer recurring questions about how a transaction was approved, changed, or recorded without manually reconstructing the history each time.
Benefits of AI agents in finance
AI agents can reduce the amount of manual work between one finance decision and the next. In Gartner's March 2026 survey of 204 finance leaders, 66% of finance organizations that had adopted AI reported greater efficiency and productivity as a top benefit [3]. The biggest benefits include:
Less manual work around exceptions
Traditional automation works well when everything follows the expected path. When an invoice does not match or a payment cannot be identified, the process often stops and somebody has to investigate.
Agents can take on more of that investigation before handing the case back to a person. Even when they cannot resolve the issue completely, collecting the relevant documents, checking likely causes, and explaining what is still unresolved can remove a significant amount of manual work.
Faster access to current financial information
Many finance processes are still periodic because a person has to start them. An agent can keep monitoring reconciliations, receivables, cash balances, close tasks, or forecast inputs as the underlying information changes. Finance does not have to wait until the next scheduled review to discover that something has moved.
Less time spent gathering context
A lot of finance work happens before the actual decision. Someone looks for the invoice, checks the PO, finds the prior entry, opens the customer record, searches for an approval, and reads the policy before they can decide what to do. Agents can gather more of that context as part of the workflow.
More capacity as the business grows
More customers create more invoices and collections work. More entities create more reconciliations. More transactions create more exceptions. Agents can take on more of the processing, monitoring, and preparation around that work, which makes it possible for the finance team to handle more activity without manual workload increasing at exactly the same rate.
How accurate are AI models at finance work?
AI agents can only be as useful as the models and systems behind them. In finance, an answer can sound perfectly reasonable and still be wrong on the account, period, amount, or accounting treatment.
DualEntry's 2026 Accounting AI Benchmark tests leading models on 101 task-based accounting questions across transaction classification, journal entry creation, accounts payable, accounts receivable, bank reconciliation, financial reporting, month-end close, and AI accounting knowledge. Grading is deterministic, a binary right-or-wrong decision, rather than asking another model to decide whether the response sounds right.
The important takeaway is not which model happens to sit at the top of the leaderboard. Models change quickly, and so do their results. What matters more is that the benchmark computes accuracy for each category of accounting work separately, because one overall number can hide how a model performs on any single category.
A model that performs well on a structured classification task may behave very differently when it has to prepare an entry or work through a close adjustment. That is why one overall benchmark score cannot tell a finance team how much of a particular workflow should be automated.
Production adds another layer. A real agent may have access to company policies, source documents, live financial data, approval workflows, and validation rules that an isolated benchmark does not. At the same time, the stakes are higher because the agent may be able to act on its answer rather than simply return one.
The better test is how the complete system performs on the workflow you actually want to automate, including the messy cases that do not follow the normal path.
Risks of AI agents in finance
An AI system that can take actions creates a different set of risks from one that only produces an answer.
The risk generally rises with the amount of authority the agent receives. An agent that identifies possible reconciliation matches is very different from one that can post entries, change vendor information, or initiate a payment.
Gartner makes the same point in its guidance for CFOs, warning that errors can propagate quickly when oversight is unclear, because agents plan and execute steps across multiple systems [5]. Its recommendation is to start with contained workflows and put governance around the agent before increasing its autonomy. Gartner has separately predicted that over 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value, or inadequate risk controls [6].
Human review also has to mean something. If somebody is approving hundreds of AI-prepared items without looking at the evidence behind them, having a person technically "in the loop" does not provide much protection.
Where AI agents fit in the finance tech stack
There are a few ways to add agents to a finance function. The main difference is how close the AI sits to the systems where the financial work actually happens.
A general model can work well for analysis and preparation where the AI does not need to touch live records.
Bolt-on agents go further by connecting directly to the systems already in the finance stack. That can be useful if the company wants to keep its existing ERP, but it also means the team needs to understand where permissions, approvals, and the audit trail live.
A native agent works inside the finance platform itself. That becomes more important as the AI moves from analyzing information toward creating or changing financial records, because the accounting data, workflows, and controls can stay in the same system.
Put AI agents to work with DualEntry
AI agents become much more useful when they can work from the same data and workflows as the finance team. Otherwise, someone still has to move the work back into the ERP, check what changed, and push the process forward manually.
DualEntry brings that work into the ERP itself. Its AI can support reconciliation, accounts payable, close management, reporting, and cash workflows using the financial data already in the system.
For example, DualEntry's reconciliation software can suggest transaction matches, draft matching rules for an accountant to approve, flag anomalies, and surface the exceptions that still need review. In AP, DualEntry can capture invoices, route approvals, and use three-way matching to compare purchase orders, invoices, and receipts before discrepancies move further through the process.
Tasks gives agents and teammates one place to manage the work those processes create. Bank matches, anomaly alerts, bank-connection issues, close-checklist status, and completed agent runs surface there, while automated categorization suggests coding for transactions as they land and scheduled agents prepare recurring reviews such as newly overdue invoices or close-status summaries for a person to act on.
DualEntry also brings AI into reporting and cash management. Teams can analyze variances, drill into the transactions behind them, and use treasury management tools to monitor cash across entities, banks, regions, and currencies.
And the agent does not have to get the final say. Approval workflows support granular permissions, multi-step approvals, period locks, staged transactions, and audit trails, so finance teams can decide what moves automatically and what still needs review.
The goal is not to put finance on autopilot. It is to let more routine work move on its own while keeping accountants in control of the decisions that still need judgment.
AI Agents in Finance FAQs
What can AI agents do in finance?
AI agents can support work across accounting, AP, AR, FP&A, treasury, and finance operations. Common use cases include reconciliation, invoice processing, cash application, collections, forecasting, variance analysis, cash positioning, close coordination, and policy checks. How much of each workflow an agent should handle depends on the information available, the consequences of getting it wrong, and the controls around the process.
What is the difference between generative AI and agentic AI in finance?
Generative AI produces an output such as an answer, summary, or draft. Agentic AI can carry the work further by gathering information, using tools, choosing between permitted next steps, and taking actions toward a goal.
What finance processes are best suited to AI agents?
The strongest candidates tend to be repeatable, high-volume workflows with clear inputs and outputs and an obvious point where a person can step in. Invoice processing, reconciliation, cash application, close coordination, and recurring finance analysis are good examples because teams can define what success looks like and check the agent's work afterward.
Are AI agents safe to use in finance?
They are, but the controls need to match what the agent is allowed to do. An agent that reads transactions and suggests matches creates less risk than one that can post records or change payment information. Permissions, approval limits, exception handling, traceability, and human review should become stronger as the agent receives more authority.
Can AI agents replace finance teams?
AI agents can take on more of the processing, monitoring, investigation, and preparation that finance teams currently handle manually. They still depend on people for business context, judgment, policy decisions, review, and accountability. The more useful shift is that finance professionals can spend less time moving routine work through the process and more time on the decisions that actually need them.
How should a finance team start using AI agents?
Choose one contained workflow, decide what the agent should and should not be allowed to do, and test it on enough real work to understand where it succeeds and where it needs help. Once the team trusts the results and understands the exceptions, it can expand the agent into adjacent parts of the process rather than trying to automate everything at once.
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Woosung Chun is the CFO of DualEntry with experience in corporate finance, accounting, strategy, and acquisitions. He previously grew from scratch and led the M&A and Finance teams at Benitago, where he completed more than 12 acquisitions in 2 years. He graduated with a BS from NYU Stern. At DualEntry, Woosung writes about AI in accounting, revenue recognition, foreign currency accounting, hedge accounting, and ERP modernization for finance teams navigating complex, multi-entity environments.
