Leveraging Machine Learning To Avoid Employee Expense Fraud

The use of machine learning in fraud detection enables businesses to track down incorrect reimbursement claims of their employees effectively.

Expense fraud involves employees showing fake documents to wrongly claim reimbursements for their supposed expenses. Such actions are considered fraudulent when the expenses are either fake or lesser than the amount of reimbursement claimed by individuals. In 2016, a study found that expense fraud cost US businesses approximately US$2.8 billion per year. In 2018, another study found that reimbursement fraud cost businesses a median expense of US$31,000 per incident. The time taken for companies to get a whiff of such incidents was found to be 24 months. Unfortunately for businesses, reimbursement fraud has been occurring consistently as they are incredibly hard to detect and prevent. One of the reasons for that is that employers do not have a lot of control regarding their workers’ expenditure—a study found that 42% of workers in North America preferred to book travel arrangements directly on the service provider’s website rather than their company’s travel unit. The use of machine learning in fraud detection offers a ray of hope to organizations to reduce and even eliminate employee expense fraud.

Machine Learning in Fraud Detection: Detecting Red Flags

Mainly, employee expense fraud can be classified into three main types—workers claiming reimbursements for unused resources, repeated claims for a single expenditure and refund claims for non-existent expenditures. Tools based on machine learning in fraud detection can screen the expense receipts of workers to find red flags before tagging them for deeper evaluation by a team of auditors. Additionally, the reasons for tagging suspicious receipts will be specified to enable auditors to find if they are fraudulent or not.

Machine Learning in Fraud Detection: Enabling Correct Sanction of Payments

Machine learning-based modules analyze data on receipts before comparing it with products and services online to tally the prices. Organizations can configure machine learning algorithms to detect outliers, such as employees visiting a concert instead of a client lunch. Based on a company’s reimbursement policy, machine learning-based tools can classify anomalies in expense records that will later be verified further by auditors or chartered accountants.

Here’s how machine learning tools prevent losses: Firstly, the financial data on scanned receipts—hotel bills, boarding passes—is captured via computer vision cameras before the algorithms detect patterns of repetition and fraud in the data. Pattern recognition is also useful for identifying areas where abnormally large amounts of money were spent. AI tools then provide insights to the financial team in organizations so that they can outright reject incorrect claims or make reimbursements within limits.

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The involvement of AI in financial operations is not an entirely new concept. Already, businesses can utilize AI for managing their cash flows and expenses effectively. The involvement of machine learning in fraud detection is also a concept that promises to become increasingly popular in the future.