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UK firms warned following HMRC AI-powered tax crackdown

Businessman works on laptop Showing business analytics dashboard

HMRC is increasingly using AI and data analytics to identify businesses and individuals for tax investigations. Photo: pcess609/iStock


Recent figures from HM Revenue and Customs (HMRC) highlight growing reliance on technology to identify non-compliance and target its investigations work, with experts predicting the trend will accelerate as the department expands it use of data and recruits additional compliance officers.

HMRC used artificial intelligence (AI) and advanced data analytics to helped protect and record £10 billion in tax revenue during 2025-26, as the tax authority achieved a record £50.2 billion compliance yield despite narrowly missing its annual target, according to its latest annual report and accounts (329-page / 11.06MB PDF). The report also confirmed that HMRC is ahead of plans to recruit an additional 5,500 frontline compliance officers by 2030 as part of efforts to reduce the tax gap.

Ian Robotham, tax law expert at Pinsent Masons, said: “The figures demonstrate the increasingly important role of technology in HMRC’s enforcement activity. AI and other data analytics are now making a major contribution to HMRC’s tax investigation work.”

Robotham said HMRC’s long established Connect data analytics platform had given the department a significant advantage.

“HMRC’s Connect system has built up a trove of valuable data and it is now able to deploy AI more to produce a target list of businesses and individuals to investigate,” he said.

While it was a record year for total yields from investigations, HMRC’s increase in the amount of tax it protects through compliance work has been slowing. HMRC marginally missed its £50.4bn target for total yield from compliance work to achieve £50.2 billion. That is a 4.6% increase over the last year compared to a 15% increase last year and 23% increase in the year before. The annual report shows HMRC collected £966.4bn in tax revenues in total in the previous tax year, an increase of £90.4bn.

Large businesses remain the biggest source of compliance returns. Corporation tax investigations involving large businesses generated £6.45bn, while VAT investigations into large businesses produced £4.59bn, making them the two most productive compliance categories. HMRC has pointed out that when it identifies underpaid tax from large companies, it is much more likely to receive that money.

The report also points to continuing concerns over tax debt. Outstanding tax debt rose from £44bn to £44.7bn during the year, although HMRC said it resolved almost £102bn of debt and expects debt as a proportion of tax receipts to fall over the spending review period. In addition, debt collection is becoming an increasingly important focus for the department as it seeks to improve collection rates and secure additional revenue.

Separately, Penny Simmons, who specialises in tax risk management at Pinsent Masons, highlighted changing risk patterns within HMRC’s large business directorate, particularly in the construction and automotive sectors.

“It is particularly interesting to note the shift in focus in the sectors being targeted within the large business unit over the last year. The tax under consideration, which guides HMRC’s focus of attention in terms of future investigations, highlights a doubling of tax risks in the automotive and construction sectors since last year, and Construction Industry Scheme (CIS) tax risk has been identified as a specific risk this year,” she said.

Simmons added that recent legislative changes, notably new rules combatting tax avoidance by umbrella companies and changes to the CIS rules, “may disproportionately impact the construction sector, which is heavily reliant on temporary labour and where there is widespread use of umbrella companies”.

“Construction businesses should expect greater scrutiny by HMRC and should strengthen oversight of their labour supply chains and ensure appropriate contractual protections are in place,” she said.

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