AI agents can automate three of the most time-consuming month-end close tasks: accrual reversals, transaction matching, and flux analysis. Instead of pulling spreadsheets and comparing lists manually, finance teams can configure agents in natural language, review their recommendations, and post entries directly to the ERP, all while keeping full control and audit visibility.
Month-end close has a dirty secret: even finance teams running modern ERPs still spend days on tasks that are largely mechanical. Pulling accrual lists, reconciling clearing accounts, building flux reports: these are not complex judgment calls. They are repetitive, high-volume processes that eat into the time your team should be spending on analysis and strategy.
In a recent live demo, Nominal's VP of Product, Yaara Hendel, walked through exactly how AI agents tackle these three workflows in real time. Watch the full recording here.
The result is not just faster closes. It is a fundamentally different way of working, where automation handles the matching and the number-crunching, and your team focuses on review, exceptions, and decisions that actually require human judgment.
Whether you caught the live session or are coming to this topic fresh, this post breaks down each workflow in detail so you can understand exactly how the automation works and what it means for your team's close process.
Why These Three Tasks Still Drain Your Close
Most ERPs were not built for the volume and complexity that modern multi-entity finance teams deal with. They store and process transactions, but they do not interpret them. That gap is where manual work lives.
Accrual reversals require someone to pull the accrual list, pull the incoming invoices, and manually compare the two. Transaction matching, especially for intercompany reconciliations and clearing accounts, often means maintaining one Excel file per account and re-exporting data every time the GL changes. Flux analysis gets rushed or skipped entirely because building a meaningful variance explanation takes hours, not minutes.
None of these tasks are intellectually demanding. But all of them are time-sensitive, error-prone, and surprisingly hard to delegate without a long onboarding process.
What AI Agents Actually Do in a Month-End Close
Before getting into the specifics, it helps to understand what "AI agent" means in a practical accounting context. These are not chatbots you ask questions to. They are configured workflows that monitor your GL, apply logic you define, and surface recommendations for your team to review and approve.
The key distinction is that nothing posts to your ERP automatically. Every output, whether it is a reversal entry, a matched transaction, or a variance explanation, goes through human review first. The automation handles the legwork. Your team handles the sign-off.
Configuration happens in natural language. Think of it as writing instructions the same way you would onboard a new team member: "When a vendor invoice hits the AP account, find the related accrual, confirm it has not already been reversed, and create a recommended reversal entry." The system follows that logic exactly, every time, without anyone having to remember to run the process.
How to Automate Accrual Reversals with Trigger Agents
Accrual reversals are one of those tasks that seem straightforward until you are dealing with hundreds of vendor bills across multiple entities. Here is how the manual process typically breaks down, and what a trigger-based automation does differently.
The Manual Process and Where It Breaks Down
The standard workflow is familiar to most controllers: accrue for a vendor bill, wait for the invoice, pull both lists at month-end, match them up, and post the reversal. The problem is that this only happens when someone has time to do it, which usually means the end of the month, when the team is already stretched.
Anything that falls through the cracks in the comparison either gets missed or requires a manual correction later.
How a Trigger Agent Handles This in Real-Time
A trigger-based configuration responds the moment a new transaction hits your ledger. As soon as an incoming vendor invoice lands in the AP account, the automation evaluates it, identifies the matching accrual, confirms it has not already been reversed, and creates a recommended journal entry. That recommendation is then assigned to the appropriate preparer and reviewer as a task.
Instead of spending time building and comparing two lists, your team simply receives a task with the recommended output and decides whether to approve, override, or dismiss it. The review takes minutes. The matching and drafting happened automatically, throughout the month.
Configuring Matching Thresholds and Instructions
One of the most common questions about this type of automation is how to handle cases where the invoice and the accrual are close but not exact. The answer is that the matching criteria are fully configurable. You can instruct the system to accept matches within a 10% variance of the bill amount, or within a fixed dollar range, or only when both transactions share the same vendor and department tag. The logic is yours to define, and it can be updated at any time without rebuilding anything from scratch.
How to Automate Transaction Matching with Matching Agents
Transaction matching covers a wide range of reconciliation scenarios, from intercompany balances to clearing accounts. The section below walks through why this is one of the most persistent pain points in the close and how automation changes the dynamic.
Why Intercompany and Clearing Account Reconciliation Takes Days
For multi-entity teams, intercompany reconciliation is consistently one of the biggest time sinks in the close process. The challenge is not just volume. It is that the data keeps moving. New transactions come in, adjustments get posted, and every time the GL changes, any static spreadsheet becomes outdated. Teams end up re-exporting constantly and still finishing the period with unresolved discrepancies.
How Matching Automation Recommends, Flags, and Refreshes Results
Matching agents can run multiple reconciliation logics simultaneously, from exact one-to-one pairings to more complex one-to-many scenarios. High-confidence results, where amounts align perfectly, and all criteria are met, can be configured to clear automatically without requiring upfront human review. Discrepant items, where there is a variance, get flagged for investigation rather than buried.
What makes this particularly useful is that the results refresh automatically as the GL changes. A pairing that showed a discrepancy earlier in the month may resolve itself when a correcting entry is posted. The system updates in real-time, so the reconciliation report you look at on day 10 reflects the current state of the ledger, not a snapshot from last week.
Handling Discrepancies and Running Resolution Workflows
When a discrepancy does need attention, teams can open a resolution flow directly within the platform. That might mean collaborating with a colleague to investigate the source of the variance, adding transaction lines manually, or triggering a resolution workflow that drafts the correcting journal entry automatically. Once reviewed and approved, that entry posts back to the ERP, and the match closes out.
How to Automate Flux Analysis with Analytical Agents
Unlike trigger and matching automation, which act on transactions, flux analysis is about interpretation. This is where an analytical layer earns its place in the close, turning raw GL data into variance explanations your team can actually use.
Why Most Teams Rush or Skip the Flux Report
Flux analysis is often treated as a final step, something to produce once the numbers are locked. The reason is simple: building a meaningful variance explanation requires pulling transactions, grouping them by driver, and writing a narrative that finance leadership can actually use. That takes time that most teams do not have mid-close.
How Analytical Automation Surfaces Drivers and Material Transactions
A flux agent built on top of your GL data can run this process continuously. Rather than waiting until the books are closed, it scans transaction-level data, including descriptions, dimensions, cost centers, and amounts, to explain what drove changes in account balances. The output is a structured explanation that highlights the most material transactions and flags one-off items that may have caused unusual fluctuations.
The explanation can be adjusted to match the level of detail your stakeholders expect, whether that is a two-sentence summary or a full driver breakdown.
Using Flux Analysis Throughout the Month, Not Just at Close
Because the analysis refreshes as new transactions are posted, it becomes a daily navigation tool rather than a deliverable you produce at the end. Finance teams can use it from day one of the close to identify where the biggest gaps are, what accounts need attention, and where the reconciliation work should be prioritized. Once a team member signs off on the explanation for a given account, it locks and stops refreshing, signaling that the work for that account is complete.
Human Control and Audit Readiness Are Non-Negotiable
Automation in accounting only works if finance leaders can trust what it produces. Every action taken by these workflows is logged in a full audit trail, including what logic was applied, who reviewed it, and what was approved or overridden. Nothing posts to the ERP without a human sign-off, and the system is SOC 1 compliant.
The goal is not to remove humans from the close. It is to make sure the work that lands on a human's desk is the work that actually requires their judgment.
Accrual reversals, transaction matching, and flux analysis represent three of the highest-effort, lowest-judgment tasks in the month-end close. Automating them does not mean losing control. It means spending less time on the mechanics and more time on the decisions that move the business forward.
If you want to see how Nominal's platform handles these workflows for multi-entity finance teams, book a demo, and we will walk you through it live.
