Introduction
Artificial intelligence is increasingly being integrated into finance operations, from transaction monitoring to expense management and fraud detection. But as businesses deploy AI to review growing volumes of employee spending, the ability to explain why a transaction was flagged is becoming as important as the detection itself.
According to Deloitte’s Q2 2026 CFO Survey, 73% of UK chief financial officers are optimistic about AI’s ability to improve business performance, while nearly all expect investment in digital technology to continue increasing in the coming years.
For expense fraud detection, that creates a governance challenge. AI systems may identify unusual transactions quickly, but finance teams still need to understand the reasoning behind a flag before taking action, particularly when decisions must be supported by internal controls, audits or compliance reviews.
Richard Jones, vice president of product at ExpenseIn, argues that finance departments should place greater emphasis on explainable AI and pattern recognition rather than relying exclusively on opaque models that produce alerts without sufficient context.
Why Explainability Matters in Expense Fraud Detection
Expense fraud is part of a broader occupational fraud problem affecting organisations worldwide.
The Association of Certified Fraud Examiners’ Occupational Fraud 2026: A Report to the Nations examined 2,402 real-world fraud cases across 143 countries and reported total losses of more than $3.4 billion. Asset misappropriation, the category that includes expense-related fraud, appeared in 90% of the cases analysed.
Expense claims can contain relatively simple warning signs, including missing receipts, duplicate submissions or spending that does not comply with company policies. The challenge becomes more complicated when finance teams need to determine whether an unusual transaction represents genuine misuse or simply an exceptional but legitimate expense.
An explainable system can help by providing the reasoning and supporting context behind an alert. That allows a finance professional to investigate the underlying transaction rather than treating the AI output as a final determination.
The Limits of Black-Box Fraud Models
Traditional automated fraud detection can be highly effective at identifying transactions that deviate from established patterns. However, a model that simply labels a claim as suspicious without explaining the factors behind the decision creates another problem for finance teams.
A black-box alert can leave reviewers asking several questions:
- What caused the transaction to be flagged?
- Which spending pattern was considered unusual?
- Was the alert triggered by the amount, timing, merchant or frequency?
- Is there evidence of duplicate or repeated activity?
- Can the reasoning be documented for an audit?
Without sufficient answers, finance teams may have difficulty validating decisions or demonstrating that internal controls were applied consistently.
Explainability therefore changes AI’s role from an automated decision-maker into an analytical support tool. The system identifies relevant evidence, while human reviewers retain responsibility for determining what that evidence means.
Where AI Can Help Finance Teams
The biggest operational advantage of AI may come from prioritisation rather than replacing human review.
Large organisations can process thousands of expense submissions, making it impractical for finance teams to investigate every transaction with the same level of scrutiny. Manual reviews can also require significant time spent checking claims, examining logs and reconciling payments.
AI can narrow that workload by identifying transactions that warrant additional attention.
Detecting Unusual Spending Patterns
A system can compare current transactions with historical spending behaviour and identify deviations from normal patterns.
Examples may include:
- Repeated claims submitted by an employee.
- Transactions consistently positioned just below approval thresholds.
- Spending that differs materially from an employee’s historical pattern.
- Unusual activity within a particular expense category.
- Duplicate claims or other inconsistencies in supporting documentation.
The purpose is not necessarily to classify every unusual transaction as fraud. Instead, the system can direct finance professionals toward claims where additional investigation may be justified.
Moving From Detection to Prevention
Fraud detection is often treated as a reactive process: an expense is submitted, the system identifies a problem and the finance team investigates it.
Historical spending data can potentially provide a broader view.
By analysing patterns across employees, departments and expense categories, AI systems can identify changes that may warrant earlier intervention. A sudden shift in spending behaviour, for example, could prompt additional review before a pattern develops into a larger financial-control problem.
This approach moves expense monitoring closer to continuous risk assessment. Rather than waiting for a clearly problematic claim, finance teams can use changes in spending behaviour to identify where controls may need to be strengthened.
Human Oversight Remains Essential
Explainable AI does not eliminate the need for finance professionals.
Expense transactions can have legitimate explanations that automated systems may not fully understand. An employee may incur an unusually large expense because of a business event, an international trip or another exceptional circumstance.
Human reviewers can assess that context and distinguish between an anomaly and potential misuse.
For this reason, AI-generated alerts should generally be treated as investigative signals rather than automatic findings of fraud. The finance function remains responsible for reviewing evidence, applying company policies and documenting decisions.
This human-in-the-loop approach can also reduce the risk of excessive reliance on automated outputs.
Building Stronger Audit Trails
The ability to document why a transaction was reviewed is particularly important for organisations with formal financial controls.
An explainable fraud-detection system can contribute to an audit trail by connecting an alert to the underlying transaction characteristics and spending patterns that triggered the review.
That can make it easier for finance teams to demonstrate how suspicious activity was identified and how subsequent decisions were reached.
The distinction is important: detecting an anomaly is not the same as proving fraud. A well-designed system should help preserve that distinction rather than converting an automated prediction into an unsupported conclusion.
The Outlook for AI in Expense Management
As corporate spending volumes increase and finance departments continue investing in automation, AI is likely to become a larger component of expense-management controls.
The central question will increasingly be how these systems are deployed rather than whether AI is used at all.
Black-box models may identify anomalies at scale, but explainable systems offer finance teams greater visibility into the evidence behind an alert. That is particularly relevant where transactions must be reviewed, challenged and ultimately defended through internal governance or external audits.
For organisations adopting AI for expense fraud detection, the more sustainable model is therefore likely to combine automated pattern recognition with transparent reasoning and human oversight.
Conclusion
AI can significantly reduce the manual workload involved in reviewing large volumes of expense transactions, but automated detection alone does not resolve the governance challenge.
Explainable AI gives finance teams a way to understand why transactions have been flagged, investigate anomalies using evidence and maintain a clearer audit trail. Human judgement remains necessary to establish context and determine whether unusual spending represents legitimate activity or potential fraud.
As AI adoption expands across finance functions, the value of fraud detection will depend not only on how many anomalies a system identifies, but also on whether finance professionals can understand and act on its findings.
