How finance teams can catch fraud early and increase audit efficiency
Fraud is a major challenge for finance teams, with the Association of Certified Fraud Examiners (ACFE) reporting that businesses lose around five per cent of their annual revenue to fraud. Expense fraud, in particular, remains a significant concern, as some employees submit duplicate claims, mischaracterise personal expenses, or falsify receipts. Others may unintentionally make mistakes that result in non-compliant spending.
Economic pressure increases the likelihood of fraud, as job insecurity and rising costs push some employees to take risks they might not have considered before. At the same time, generative artificial intelligence (Gen AI) and advanced editing tools make falsified receipts harder to detect. Stronger internal controls, automation, and artificial intelligence (AI) can help finance teams catch both deliberate fraud and human error, reducing risk and financial losses.
Businesses of all sizes can reduce fraud risk with stronger internal controls. Automation, clear approval workflows, and spending limits prevent fraudulent claims before they happen. Blocking non-compliant transactions, rejecting personal expenses, and capping meal and travel costs all help reduce financial exposure, while setting up pre-approval workflows for high-risk expenses lets finance teams flag suspicious claims before reimbursement.
Falsified receipts are a common issue, with some employees altering amounts, changing dates, or submitting the same receipt multiple times. Automation reduces these risks by pulling transaction data directly from corporate credit cards, travel providers, and merchants, preventing employees from editing or duplicating transactions. AI-powered tools add another layer of protection by scanning expense reports for red flags, such as frequent claims just below policy limits, sudden spending spikes, or repeated expense patterns. Cash reimbursements, which are harder to track, also require closer review.
AI helps businesses of all sizes detect fraud faster and with more accuracy, as it picks up patterns that manual reviews miss. For example, AI will highlight expenses that consistently fall just under approval limits, high-value transactions, and frequent purchases from questionable vendors. Finance teams can then focus on the transactions that need a second look instead of reviewing every single claim. AI-powered image recognition also flags altered receipts by spotting mismatched fonts, changed amounts, and other signs of manipulation.
However, fraud prevention requires more than detection, and businesses must continuously evolve their policies as fraud tactics change. Hybrid work models introduced new expense categories like home office equipment and internet reimbursements, requiring finance teams to clearly define which expenses qualify and set spending limits. Regular audits can help finance teams uncover fraud, prevent policy abuse, and maintain compliance. Reviewing a sample of expense reports each month, comparing transactions against policy rules, and monitoring high-risk categories like entertainment and travel all help reduce financial risk.
Many employees don't intend to commit fraud and simply misunderstand the rules. Clear communication makes a difference, educating employees about which expenses meet company policies. Keeping guidelines easy to find, sharing updates when policies change, and offering regular training all help. Finance teams see fewer policy violations and better compliance when employees know what's expected.
Preventing fraud improves compliance, protects company revenue, and strengthens audit efficiency. Blocking unauthorised transactions, automating receipt validation, and using AI to detect fraud all reduce risk and financial losses, while regular audits, well-defined policies, and ongoing training create a system where fraud is harder to commit and easier to catch. Fraud tactics will continue to evolve, and businesses of all sizes must stay proactive and leverage the latest technology to safeguard their financial integrity.
