Expense fraud represents a significant economic loss for companies, and it is essential to identify and prevent it. Discover how artificial intelligence can help you do it.
According to a study made by Captio in 2017, expense fraud amounts to 53.883€ on average per company, that is 700€ per employee per year. This is why we understand why internal fraud on travel expenses can be an important concern for companies: it may amount to 12% of the total reimbursements. Detecting this type of fraud before it is reimbursed is thus an important challenge.
Most frequent fraud cases
Some employees are very resourceful when it comes to committing expense fraud. One of the most frequent cases is that of “inflated” expenses. Indeed, mileage allowances are a true gold mine for them. Reimbursement depends on the distance covered and the vehicle’s tax horsepower. Employees committing fraud will inflate either of these. It is the easiest way to be reimbursed way more than what you are owed.
This practice also works on other expense categories, such as taxi fare. Many drivers give handwritten receipts, so the employee will ask them to inflate the fare to earn the difference. Another easy way to fraud is to duplicate expenses. This happens when the same expense report is claimed twice: the employee then gets reimbursed twice.
Of course, this is not a complete list: there are many other ways to commit expense fraud. Companies thus must detect it before the reimbursement stage.
Detecting fraud using Artificial Intelligence
Expense fraud detection has always been a demanding process, especially done by hand. An automated alternative is clearly needed. Indeed, automating this task will reduce costs and improve a company’s financial results. That is why some companies have turned to Jenji to organise their expense management policy.
Today, many specific software solutions can be used to detect and prevent fraudulent expenses. Jenji allows for an in-depth analysis of expenses to detect anomalies. This is made possible by Artificial Intelligence, which can understand and learn how human intelligence analyses situations.
An AI engine can assess whether an expense is legitimate or fraudulent in a few steps. First, Machine Learning can be used to understand the reimbursement context. This allows the information to be extracted by digitising documents. This specific data can then be searched through and compared using a broad set of external data. This helps check the validity of each retailer, of their prices and other basic information, and to verify the legitimacy of all expenses. The Machine Learning system detects repetitions and trends and fraud is detected in less than a millisecond. For instance, comparing spending between employees helps detect irregularities: expenses that are superior to those of other employees, unusual, or filed on a Sunday, and also suspicious mileage allowances.
Expense claims are approved or rejected on the basis of this research. One of Jenji’s features is a real-time visual alert which appears when an employee files an expense from a specific category and it exceeds a preestablished threshold. The alert system also activates when it detects a duplicate expense. If a red flag warning is detected, the expense can immediately be checked thanks to expense accounts digitisation.
Better safe than sorry
Given the economic importance of expense fraud, it is extremely important to prevent it. This can be done through human resources training, which is essential to prevent it. Special attention should also be devoted to work climate, which play an important part in the advent of internal fraud.
For more details on Jenji Vault, don’t hesitate to contact the Jenji team at firstname.lastname@example.org. We will be glad to assist you in setting up your project.