AI in Finance
AI Audit Preparation: How to Use It From the Inside
16 February 2026
Almost everything written about AI in audit is written from the auditor’s side of the table. How AI tools let auditors analyse full transaction populations. How audit firms complete fieldwork faster. How the Big 4 are deploying machine learning across data sets that would have taken weeks to sample manually.
That perspective is useful if you are an auditor. It is not the perspective most finance teams need.
This post is written for the finance team being audited. If your external auditors are now using AI tools in their fieldwork, and most major firms are, the way you prepare for audit needs to change. The old approach was designed for a sample-based world. AI-assisted audit does not work that way. Finance teams that understand the shift prepare differently. Finance teams that do not find out during fieldwork instead of before it.
How AI changes what auditors expect
Traditional audit was built around sampling. Auditors could not review every transaction, so they selected a statistically representative sample and drew conclusions from it. A transaction that fell outside the sample was invisible to the audit process. Finance teams understood this, consciously or not, and prepared accordingly: making sure the transactions most likely to be sampled were fully supported.
AI-assisted audit changes this completely.
Full population testing is now standard practice at most major audit firms for transactional data. Instead of sampling 200 journal entries from a population of 50,000, auditors run their AI tools across all 50,000 and flag every anomaly. Every unusual posting. Every journal entry that does not fit the expected pattern. Every transaction where the supporting documentation does not match the amount, the date, or the counterparty.
The practical implication is significant. Isolated unusual transactions that would never have appeared in a traditional sample are now visible. One-off adjustments posted at quarter end. Intercompany transactions with incomplete documentation. Manually-posted entries without standard approval workflows. These were previously low-risk from an audit perspective because the probability of selection was small. They are no longer low-risk.
Finance teams that understand this adjust their preparation to reflect the full-population world, not the sampling world. The question is no longer “is this likely to be selected?” The question is: “would I be comfortable if every auditor saw every transaction?”
This also changes the nature of auditor queries. AI tools flag anomalies and generate queries automatically. A finance team that has already reviewed its own data for anomalies and prepared explanations or corrections will move through fieldwork far faster than one that is seeing the query list for the first time during the audit.
Using AI in your own audit preparation
The same technology auditors are using to analyse your data is available to you. The finance teams getting the most from audit preparation are the ones who run their own AI-assisted review before fieldwork begins.
Reconciliation AI can ensure every balance sheet balance is fully supported before auditors arrive. Automated reconciliation tools that flag unreconciled items, aged items, and unusual entries compress the pre-audit reconciliation process from weeks to days. The goal is to arrive at fieldwork with a clean balance sheet: every item explained, every reconciling difference documented, nothing outstanding that you do not already understand.
This matters more than it used to. If your AI tools have already identified and resolved an unusual balance, and the auditor’s AI tools then flag the same balance, you can provide an immediate explanation with supporting documentation. The fieldwork conversation takes five minutes instead of two days.
Document extraction AI can organise supporting schedules faster. Invoice processing tools, contract analysis tools, and document classification tools can pull together audit packs that would previously have required significant manual effort. Period-end supporting schedules, lease documentation, loan agreements, key contracts: extraction and classification tools can organise these into structured audit packs in hours rather than days.
Anomaly detection is the most powerful application. Running your own anomaly detection across the transaction population before audit starts means you find your own high-risk items first. This is always better than having auditors find them.
Every finance function has unusual transactions in it. Legitimate ones, usually, but ones that require explanation. The difference between finding them in your own review and having them flagged in the auditor’s AI output is the difference between walking into audit with documented explanations and walking in with questions you cannot immediately answer.
The audit preparation without wrecking your quarter framework is the broader context for this approach. The AI tools are an accelerant on top of the underlying discipline, not a replacement for it.
The data quality implication
AI-assisted audits are faster and more comprehensive than sample-based audits. This combination has a specific implication for data quality: weaknesses that were previously survivable because they fell outside the sample are now visible.
Consider what this means in practice. Duplicate supplier records that never appeared in a sample now appear in a full-population vendor analysis. Inconsistent cost centre coding that was present in the population but absent from the sample is now visible across every transaction. Bank reconciliation items that have been sitting unresolved for six months because they were not material enough to prioritise are now in the auditor’s query list.
The answer is not to panic. Fix your data before fieldwork, not during it.
Data quality remediation before audit follows a clear sequence. First, run your own population analysis on the areas you know are imperfect. Supplier master data, customer master data, cost centre coding, intercompany balances, bank reconciliations. Identify the issues before the auditors do. Second, remediate what can be remediated before fieldwork starts. Resolve the aged reconciling items. Clean the duplicate records. Correct the miscoded transactions. Third, document what cannot be remediated and prepare the explanation.
The data quality in AI-assisted finance post covers the remediation approach in detail. The principle that applies here is specific: the audit is a deadline, and data quality work done before that deadline has a different value than data quality work discovered during it.
Auditors are not looking for perfection. They are looking for a finance function that understands its own data, has controls to manage it, and can explain anomalies coherently. A finance team that comes to audit with a clean data set and documented explanations for the exceptions demonstrates exactly that.
What AI can and cannot do in audit preparation
AI tools compress the time spent on mechanical work. They expand the population covered. They identify patterns that manual review would miss. These are genuine capabilities with real value in the audit preparation context.
They cannot do the judgment work.
AI cannot make accounting treatment decisions. Whether a contract is a lease under IFRS 16 or a service agreement is a judgment call that requires understanding the contract terms and the substance of the arrangement. AI tools can extract the relevant clauses from the contract. They cannot decide how to classify the arrangement.
AI cannot assess whether a provision is adequate. Flagging that a provision has not moved in three years is within the capability of an anomaly detection tool. Deciding whether the provision is appropriate given the underlying liability requires professional judgment, knowledge of the specific facts, and an understanding of management’s intentions.
AI cannot interpret context. A journal entry posted at 11:47pm on the last day of the quarter looks unusual in an AI analysis. It may be the legitimate correction of an error identified in the final reconciliation review. It may be something else. The AI flags it. The judgment about which it is, and how to document the explanation, is human work.
The practical implication for audit preparation is this: AI tools can do the mechanical work of identifying what needs to be documented, reviewed, or explained. The documentation, review, and explanation remain professional responsibilities. Use AI to find the work. Do not expect it to do the judgment work for you.
A practical audit preparation checklist for the AI-assisted audit environment
The following checklist reflects the full-population, AI-assisted reality of current external audit practice. It is designed to be run in the six to eight weeks before fieldwork begins.
Data review and remediation
Run full-population anomaly detection across journal entries. Identify and document all unusual items. Remediate errors before fieldwork. Clean supplier master data: resolve duplicates, verify active and inactive status, confirm bank details. Reconcile all balance sheet accounts to zero unreconciled items, or document explanations for aged items. Review intercompany balances for completeness and agreement.
Documentation and support
Prepare supporting schedules for all material balance sheet items. Organise lease documentation with IFRS 16 calculations. Pull together key contracts with extracted payment terms and obligations. Prepare board minutes and approval evidence for significant transactions.
Process documentation
Document your AI tools and their governance framework. Auditors are now routinely asking what AI tools the finance function uses and what oversight exists. The AI governance framework post has the structure for this documentation.
Exception preparation
Identify every item you already know will generate an auditor query. Prepare the explanation and supporting documentation in advance. Distribute query responses to the team members who own the relevant areas so they can answer immediately rather than escalating to you.
Timeline
Eight weeks out: data quality review and remediation. Six weeks out: balance sheet reconciliation sign-off. Four weeks out: supporting documentation assembled. Two weeks out: exception explanations prepared and distributed. One week out: complete walkthrough of audit pack with your team.
The AI-assisted audit environment rewards preparation more than the sample-based one did. The cost of finding a problem during fieldwork, when auditors are already in your data with tools that can follow any thread, is higher than the cost of finding and resolving it in your own pre-audit review. Run your own population analysis first. Fix what you find. Document what you cannot fix. Arrive at fieldwork with nothing outstanding that you do not already understand.
Maebh Collins is a Fellow Chartered Accountant (FCA, ICAEW) with Big 4 training and twenty years of operational experience as a founder and senior finance leader. She writes about AI in finance transformation from the inside out.