In the last column, we looked at the rise in expense control complexity and the subsequent increased burden for the finance team working with out-dated controls. Today, we look at how the AI threat can be turned into an ally by transforming sub-par expense control into accounting controls with AI.
Tackling receipt fraud made easy with AI.
AI editing tools make it easy to turn an £80 dinner expense into £180. It gets round the traditional ‘does it look right’ check, because the logo, format and VAT number are all correct. The solution is continuous, automated analysis of the expense process, or modern accounting controls, with AI. It thwarts the fraudster by checking not just for looks, but against historical spending patterns, card transactions, merchant records, and policy limits, right across the organisation.
The pattern recognition that AI is good at, can make it an anti-fraud ally. Good use of Ai and accounting controls can reduce the amount of time finance teams need to spend checking receipts, invoices and claims. It can also surface unexpected patterns that the human eye might find harder to spot. Plus, it makes continuous monitoring possible. Beyond document checks, many complementary controls are rarely implemented. With AI tools, additional controls are now easier to apply.
Accounting controls with AI check against behaviours.
For example, it can detect weak identifiers of fraud. This could be a claim for mileage allowance and fuel expenses on the same day, effectively two reimbursements for a single journey. It can flag reoccurring expenses of an identical amount and category, when submitted by the same person, a pattern that genuine receipts rarely follow. Potential duplicates can be spotted too; the same expense, same vendor, city and amount, either submitted twice by the same person or claimed separately by two different people.. This means that specialised AI accounting tools are now capable of automatically reconciling expense claims with accounting entries and financial flows.
Outside of pattern detection, AI fraud-detection tools can have a significant deterrent effect. Potential fraud is nipped in the bud because it is harder to rationalise. If everyone knows that the system is up-to-date and likely to catch them, it fundamentally changes their behaviour. Of course, to be fully efficient, these controls must be automated and continuous. Only then can control teams focus on genuinely complex cases requiring human analysis.
Comparing complementary controls, continuously
AI has made it easier to carry out expense fraud, but it can also be part of the solution. An AI- boosted accounting system can motivate the finance team with its ability to compare complementary controls. It does this with elements like:
- overlapping data to raise fraudulent anomalies that would otherwise be missed
- monitoring all submissions continuously, removing sampling and the possibility of missing some fraudulent claims
- cross-referencing data with historical records to detect anomalies and risky patterns
Then, it escalates only the ambiguous cases, with a summary of the checks performed.
Measured results are significant.
Studies show AI fraud detection reduces false positives by 30% to over 80% compared with rule-based approaches. A 2024 meta-analysis of 47 studies found reductions of 40–60%, while industry implementations have reported reductions of up to 80% in specific transaction-monitoring environments.
Using AI as your ally also helps the team. McKinsey reports a 30% to 50% reduction in manual workload in finance teams thanks to automation and agentic AI 5 . This trend is set to continue too. According to Wolters Kluwer, 44% of finance teams will use AI agents by 2026, which represents an increase of over 600% in one year.
Well-governed accounting controls with AI is next level
Finance teams know that expenses fraud is already happening in their organisations. It is being made easier with AI tools that can create near-photographic realism in a system that checks on whether something looks right.
Accounting processes with AI clearly add value by embedding controls into processes. However, it must be governed. To take your AI tool to the next level, three governance elements must be defined:
- Role allocation: who detects (the agent), who investigates (internal control), who sanctions (HR, management).
- Investigation protocol: timestamped evidence preservation, forensic analysis, interviews, decision.
- Quarterly audit reporting: flagged claims, confirmed fraud, recovered amounts, average detection time.
Then consider reinforcing your system with AI and an accounting control too. It could be set up to monitor structured data, rather than appearance, to check against external data automatically, remove hard-to-check formats, and integrate traditionally siloed internal information like cards and booking systems.
Expenses controls don’t have to be out-paced and manual. We find that companies that address potential receipt fraud issues are able to move the expenses process from a constraint to a strategic advantage. Companies that seize this opportunity will gain a stronger compliance culture, trust in finance as a governance pillar, and a defensive shield against wider AI-driven threats such as identity theft or fake communications.
AI has turned receipt fraud into an industry, with a counterfeit factory in every pocket. It’s time for Finance teams to harness their expenses process with AI and accounting control tools and turn the tables on this fraud.
The post From Data Overload to Data Insight: How AI and Modern Accounting Solutions are Transforming Expense Control appeared first on The Fintech Times.
