The old-fashioned audit process, based on sampling, paper records, and professional skepticism, applied annually, is struggling to keep pace with the present era.
It is not because auditing today has flaws that artificial intelligence in auditing is required. It’s quite possible that it’ll change the very nature of auditing. This development could not have been more timely for those accountants who grew tired of doing the same work in the same way year after year.
This guide does provide an account for professionals seeking substance.
What auditing still gets wrong
Before examining what artificial intelligence in auditing can do, consider what auditing still cannot do well without it.
Most audits examine a fraction of available data. Sampling is a compromise — a practical one, but a compromise nonetheless. A CPA testing 50 journal entries out of 500,000 is not performing a thorough review. They are performing a disciplined guess.
Material misstatements lie buried between samples, while the possibility of fraud is often right there. Then there is the human element to think about. Audit staff in the United States generally work 60 to 80 hours per week during busy times. The job can become monotonous during its most challenging periods or mentally taxing during its most rewarding moments. Burnout is a very real and measurable problem.
Artificial intelligence in accounting and auditing does not replace the judgment that auditors bring to their work. It removes the grind that prevents them from exercising.
What AI in auditing actually does precisely
“AI in Auditing” does not refer to one unified technological application. It is rather an ensemble of technologies used in different ways to address particular problems arising in auditing. The classification of such technologies may create unrealistic expectations; however, their differentiation contributes to the development of a more effective and productive toolkit.
Machine learning identifies anomalies in large datasets of transactions. It flags journal entries posted outside business hours, round-dollar transactions clustered around authorization thresholds, or unusual vendor payment patterns — across 100% of the population, in seconds.
Natural Language Processing (NLP) carefully analyzes and extracts data from various types of documentation, such as agreements, leases, meeting minutes, and regulatory documents. Tasks that took many hours of an associate’s time can now be completed in minutes and produce a structured output ready for inclusion in work papers and documentation.
Robotic Process Automation (RPA) technology performs routine tasks, such as verification, search, and reconciliation. Such automation never tires and reduces manual transcription errors.
Analytics models are used to calculate account balances by considering historical data and current transactions. In case there is any deviation from normal behavior, an audit trail will be generated much faster and more accurately than in traditional analytics.
Overall, these tools define what artificial intelligence in auditing looks like: not a single solution, but a set of technologies that shift the assurance role from reactive to proactive.
It is important because there is a difference between artificial intelligence in accounting and auditing, which includes two interlinked categories of activities. While in accounting, AI deals with classification and period-end close, in auditing, its task is to assess risk and evaluate evidence. It helps in creating an approach fundamentally different from treating accounting and auditing as separate problems.
Real-world use cases CPAs need to know
1. Full-population transaction testing
The Big Four have each invested heavily in proprietary AI and advanced analytics platforms built specifically for audit. KPMG’s Clara platform uses AI to assess risk levels and standardize audit processes across global offices. Deloitte’s Argus extracts accounting information from electronic documents using machine learning and natural language processing. EY’s Helix integrates data sources to identify trends, outliers, and patterns in financial data. PwC’s Halo uses data analytics to test information reliability and surface audit risks and anomalies.
In all cases, the strength of these models is their ability to test every transaction in the population. Instead of relying solely on sampling, the auditor may examine the entire dataset to identify potentially risky transactions and test them further. It changes the way the debate around risk is framed entirely.
Where once the question was, “Have we found the risk by using our sample?” now it becomes, “What can the whole population tell us about the risk?”
2. Continuous monitoring and real-time auditing
Some large organizations are using AI-enabled monitoring to identify control exceptions earlier. The audit process is not limited to waiting for year-end results but happens simultaneously with operational processes.
For external auditors, this opens possibilities for interim assurance engagements. For audit committees, it changes the risk posture from reactive to anticipatory.
It is one of the most consequential applications of artificial intelligence in auditing — not because the technology is sophisticated, but because the operational shift it enables is profound.
3. Lease and contract review under ASC 842
The adoption of ASC 842 created a significant manual burden for many organizations — requiring teams to review large volumes of contracts to identify embedded leases, extract key terms, and calculate right-of-use assets and lease liabilities.
NLP tools now extract lease commencement dates, renewal options, payment escalations, and discount rate inputs directly from contract language. Weeks of associate time become structured data output. It is artificial intelligence in accounting and auditing working in tandem — the accounting team uses the output to record, the audit team uses it to test.
4. Fraud detection in accounts payable
In the domain of auditing, AI has proven to be highly effective and consistent in identifying fraud committed by vendors, ghost employees, and abuse of the expense reimbursement process. The machine learning algorithm detects patterns of what is considered “normal” within the specific organization, and then identifies anomalies based on those patterns.
Researchers at the University of St. Gallen have explored deep-learning approaches for anomaly detection in journal-entry data.
In practice, Organizations have reported that AI systems have flagged patterns such as vendor payments sharing address details with employee records — signals that had remained undetected for years because traditional sampling procedures repeatedly overlooked them.
5. Audit confirmation automation
The confirmation process — sending, tracking, and reconciling bank and accounts receivable confirmations — has historically been paper-intensive, slow, and prone to follow-up failures.
Modern confirmation platforms can automate request dispatch, tracking, matching, exception flagging, and documentation. It is AI in auditing, delivering immediate, verifiable results in perhaps the riskiest process in any audit.
It is precisely what AuditConfirm is all about.
Here is a table to summarize:
| AI Use Case | Audit Area | Benefit |
| NLP Contract Review | ASC 842 | Faster lease abstraction |
| Full-population testing | Journal entries | Better anomaly detection |
| Confirmation automation | Cash/AP/AR | Faster evidence collection |
The benefits of AI in Auditing
Coverage: The application of artificial intelligence in auditing enables the testing of all transactions. AI enables broader population testing, reducing some of the limitations inherent in sampling.
Speed: What used to take several days now takes only a few hours. The audit cycle is shortened.
Consistency: Artificial intelligence in auditing applies the same rule to the first transaction and the ten-millionth. Human reviewers do not — particularly not at hour seventy of a busy season week.
Documentation: AI applications generate structured output that is easier to audit, improving workpaper quality and decreasing the time needed for review.
Risk assessment: CPAs can use their time more efficiently by focusing on matters that require professional judgment, such as related-party transactions, management estimates, going concern, and complex financial instruments.
The net effect is not a smaller audit team. It is a more effective one — with professionals deployed where their expertise actually matters.
The challenges that come with AI in auditing
Explainability remains a structural problem: Many machine learning models function as black boxes. An auditor who cannot explain why an item was flagged cannot defend that flag to a client, a regulator, or a court. The PCAOB’s July 2024 Staff Spotlight on Generative AI noted that the integration of AI in audits is in its early stages but rapidly evolving — and that audit firms must ensure human oversight of AI-generated outputs remains robust. The standards have not fully kept pace with the technology. That gap carries real professional liability.
It is the quality of training data that ultimately determines the quality of results: If an AI algorithm is trained exclusively for a particular business sector, geographic area, or accounting regime, it may not perform well outside its comfort zone. But it should be remembered that this is precisely the point – the decisive one – on which the use of AI in auditing depends. It will depend on the training data whether an AI produces either correct answers or confidently wrong ones.
The integration process is trickier than vendors claim: Many audit clients use legacy systems, such as outdated SAP or pre-cloud Oracle setups. Getting the appropriate structured data from such systems can become quite a technical problem, often underestimated for its complexity.
Independence standards apply: Once the connection between an auditor and their clients via AI tools is established, the auditor must assess independence in accordance with AICPA, PCAOB, and, where necessary, IESBA standards.
The workforce transition is real: CPAs need to understand what an AI model is doing — its inputs, its assumptions, its failure modes. Firms that treat AI in auditing as plug-and-play without investing in training will misuse it. The audit will not improve. It will simply fail faster while producing better-looking documentation.
What the evidence actually shows
The empirical record on artificial intelligence in accounting and auditing is growing, though it remains limited by the recency of large-scale deployments.
New research on auditing in the Journal of Emerging Technologies in Accounting examines the transformation of auditing procedures through machine learning and data analytics. Findings from such studies indicate a higher anomaly-detection rate than that of traditional sampling-based approaches. On the other hand, another framework for applying machine learning to internal audits throughout their lifecycle, featured in the Research in Accounting Regulation journal in 2024, found that AI-supported procedures detect risks earlier and enable human judgment to be used more purposefully.
According to recent developments in the accounting world, PwC plans to invest about $1 billion over three years in training its existing staff on AI, recruiting AI experts, and adopting AI in its business operations. During the same period, KPMG announced an investment plan of around $2 billion in AI and cloud solutions to improve its consulting, auditing, and tax services.
What the evidence does not show — at least not yet — is that AI in auditing reduces audit failures to zero, eliminates fraud risk, or removes the need for professional skepticism. Any claim to those effects should be treated as a marketing statement, not a finding.
What this means for the CPA practice
The firms gaining ground are not necessarily the largest. They are the most technologically credible.
Mid-size regional firms in the US have deployed artificial intelligence in auditing and are competing directly with larger firms on efficiency, coverage, and turnaround time. The technology has lowered barriers to capabilities that did not exist a decade ago.
This scenario offers an opportunity on one side and a duty on the other side. The opportunity here is to use AI technology in audits, along with necessary training, to ensure good audits are conducted. However, at the same time, these organizations have the responsibility of maintaining professional ethics standards and exercising judgment wherever the model fails.
Independence. Objectivity. Due to professional care. Reasonable assurance. These standards do not yield to technology. They govern it.
The CPA who understands artificial intelligence in accounting and auditing — who can interrogate a model’s output, challenge its assumptions, and apply professional judgment to its findings — is not being replaced by the technology. That CPA is the point of the entire exercise.
AuditConfirm: built for what the audit profession is becoming
AuditConfirm is an audit technology company. We build confirmation automation software — not as a side product, but as our core focus. The platform automates the dispatch and tracking of bank confirmations, accounts receivable confirmations, and legal letter requests within a workflow designed to meet the standards CPAs must follow. Responses are matched, documented, and flagged for exception follow-up. The audit trail is complete.
Our view on the use of artificial intelligence in auditing is based on practice rather than theory. AuditConfirm focuses specifically on confirmation workflows, building tools that serve the profession’s present obligations while anticipating what the next decade of AI in auditing will demand.
A final observation
The auditors who will matter most in the next decade are not the ones who adopted AI earliest. They are the ones who used it most responsibly.
Artificial intelligence in auditing does not exercise judgment. It accelerates it. The quality of that judgment — the professional skepticism, the ethical rigor, the commitment to the public interest — still belongs entirely to the CPA.
What has changed is the scale at which that judgment can now be applied.
For a profession built on trust, that is not a minor development. It is a defining one.
Disclaimer: AuditConfirm is an audit technology company that provides confirmation automation software to accounting firms and internal audit teams. This blog reflects our perspective as an active participant in the audit technology space. All regulatory references, platform names, and research citations are independently verifiable and linked where available.
FAQs
AI in auditing uses techniques such as machine learning, natural language processing, and automation to analyze the entire set of transactions rather than taking samples. The procedure helps identify anomalies, extract data from contracts, and autonomously generate confirmations.
Coverage, speed, and consistency. Artificial intelligence in auditing facilitates the analysis of all transactions, cuts days-long processes to mere hours, and uses consistent rules for millions of records. CPAs can concentrate on more judgment-based work.
Conventional auditing uses sampling and end-of-year processes. Artificial intelligence in accounting and auditing uses continuous, full-population testing to address risk identification, outlier detection, and evidence evaluation across whole databases rather than selective samples.
Explainability, data quality, and independence. AI systems often fail to provide reasons for detected abnormalities. Legacy systems pose challenges when retrieving raw information. Furthermore, deploying AI applications into client systems may require compliance with AICPA and PCAOB independence standards.
The use of AI in AuditConfirm entails sending, tracking, and flagging of bank and accounts receivable confirmations in accordance with the recommendations stipulated under AU-C 505. There are many advantages to this system, including the efficiency and accuracy of audits and the creation of an audit trail.