Fraud detection used to be something that only large enterprises and government agencies worried about. That is no longer the case. Artificial intelligence has fundamentally changed the economics of fraud detection, making it faster, cheaper, and more accessible. And it is not just businesses using AI to protect themselves. Tax authorities around the world are deploying AI to audit taxpayers with a level of scrutiny that was previously impossible. For small businesses and self-employed professionals, understanding this shift is no longer optional.
How AI Detects Fraud
Traditional fraud detection relied on manual audits, tip-offs, and rule-based systems. If an expense exceeded a certain threshold, it got flagged. If a vendor appeared on a watchlist, it got reviewed. These systems caught obvious problems but missed the subtle patterns that characterise most real-world fraud.
AI-powered fraud detection works differently. Machine learning models analyse vast datasets to identify patterns that deviate from the norm. They do not follow static rules. They learn what normal looks like for a specific business, industry, or transaction type, and then flag anything that falls outside those patterns.
Duplicate payments. One of the most common forms of accounting fraud, and one of the hardest to catch manually, is the duplicate payment. A vendor submits the same invoice twice with slightly different formatting, or an employee processes the same expense report through two different systems. AI can identify these near-duplicates even when the amounts, dates, or descriptions differ slightly. The Association of Certified Fraud Examiners (ACFE) estimates that duplicate payment schemes account for a significant portion of the 5% of revenue that organisations lose annually to fraud.
Ghost employees and vendors. AI can cross-reference payroll records, vendor databases, and tax identification numbers to identify entities that may not exist. A vendor with no physical address, no online presence, and no other clients is a red flag. An employee whose bank account matches another employee's is another. These patterns are invisible in isolation but obvious when an algorithm analyses the full dataset.
Unusual transaction patterns. Machine learning excels at spotting anomalies. A sudden spike in expenses in a particular category, a series of transactions just below a reporting threshold, payments to new vendors immediately before a fiscal year-end: these patterns can indicate anything from innocent timing coincidences to deliberate manipulation. AI surfaces them for human review.
Behavioural analysis. More advanced systems analyse user behaviour patterns. If someone who normally processes five invoices per day suddenly processes fifty, or if a user accesses financial records outside normal working hours, the system can flag the activity for investigation.
How Tax Authorities Are Using AI
This is the part that every business owner needs to pay attention to. Tax authorities are not just encouraging AI adoption. They are using it themselves, aggressively.
The OECD's 2023 Tax Administration report found that over 75% of advanced tax administrations are now using or actively piloting AI and machine learning tools. These are not experimental projects. They are production systems processing millions of tax returns.
The United States Internal Revenue Service (IRS) announced in 2024 its Agentforce initiative, deploying AI agents to assist with taxpayer inquiries and, critically, to enhance audit selection capabilities. The IRS has stated that AI is helping it identify high-risk returns with greater accuracy and efficiency than traditional methods. The agency collected an additional 1.3 billion dollars from high-income taxpayers in 2024, partly attributed to improved AI-driven audit targeting.
Her Majesty's Revenue and Customs (HMRC) in the United Kingdom has been using its Connect system for over a decade, cross-referencing data from banks, employers, property records, and social media to identify discrepancies in tax returns. The system analyses billions of data points and has been credited with recovering over 4 billion pounds in additional tax revenue.
Closer to home, EU member states are investing heavily in digital tax infrastructure. Italy's SdI e-invoicing system gives the Italian Revenue Agency real-time visibility into every domestic transaction. France and Poland are building similar capabilities. As these systems mature, the ability of tax authorities to detect discrepancies, inconsistencies, and potential fraud will only increase.
What This Means for Compliance
The practical implication is straightforward but important: the bar for record-keeping has risen permanently.
When tax authorities used manual sampling to select audit targets, the odds of any individual return being scrutinised were low. With AI, every return can be analysed against patterns, benchmarks, and cross-referenced datasets. The question is no longer whether your return will be checked, but how thoroughly.
This does not mean that every business is at risk of an audit. It means that the businesses most likely to attract attention are those with inconsistencies in their records: expenses that do not match industry norms, income that does not align with reported lifestyle indicators, or filing patterns that deviate from prior years without explanation.
Why Clean Records Matter More Than Ever
For self-employed professionals, the takeaway is not to fear AI. It is to respect what it means for the standard of record-keeping that is now expected.
Accurate categorisation matters. When an AI system compares your expense claims to industry benchmarks, miscategorised expenses can look like anomalies even when they are legitimate. Properly categorised records reduce false flags.
Consistency matters. AI looks for patterns over time. A business that reports steady income for three years and then shows a dramatic drop will attract scrutiny. If the drop is real and explainable, clean records make it easy to demonstrate. If your records are disorganised, even a legitimate explanation becomes harder to prove.
Completeness matters. Missing records are worse than imperfect records. Gaps in your financial data create exactly the kind of anomalies that AI systems are designed to detect.
Timeliness matters. Late filings, amended returns, and inconsistent reporting periods all contribute to a risk profile that AI systems evaluate. Filing on time, every time, is one of the simplest ways to stay below the audit radar.
The Bottom Line
AI-powered fraud detection is not something that only affects large corporations. It is reshaping how tax authorities interact with every taxpayer, including self-employed professionals and small businesses. The good news is that the best defence is also the simplest: maintain clean, accurate, and complete financial records.
The businesses that will navigate this new landscape successfully are not the ones trying to outsmart the algorithms. They are the ones with nothing to hide and the records to prove it.
Michael Cutajar, CPA — Founder of Accora.