According to the Association of Certified Fraud Examiners, the approximate median loss due to reimbursement fraud approximates to $40,000. Some of the ways in which fraudulent claims are made include fictitious business receipts, inflated expenses or illicit upgrades, false merchant codes, multiple claims for the same bills or codes, etc.
An example is when we purchase pillowcases at Walmart, we expect to see the retailer spelled out distinctly in our bank statements. However, when it comes to the gentlemen’s club industry, expenses are not so obvious.
Irrespective of whether these claims are a part of generic mistakes or intentional fraud, they lead to heavy financial impact for all types of organizations.
Conventional methods of fraud detection perform random sampling which generally covers only 1 to 15% of the total reimbursements. Real-time fraudulent detection can be performed through the usage of AI. Below are some key steps that any AI engine would need to adopt to classify whether an expense is legitimate or fraudulent expense.
- Extracting information: Machine learning, computer vision, deep learning, and NLP can be used to understand the context of reimbursements by scanning receipt images, boarding passes, travel documents, etc.
- Data Augmentation: These data can be searched and matched in real-time against thousands of external and social data sources to establish the validity of business merchants, their pricing, and background information to confirm the submitted expense reports. This can be also used to detect whether the business that issued the receipt was a club, casino, etc. Based on this background research, reimbursement requests can be approved or rejected. In order to ensure that a specific alcohol is not being billed into the restaurant bill, the application can also match each item in the bill against a dictionary of thousands of brands of alcohol. Additionally, a guest claim on the T&E can be searched against multiple news and government sites to reduce the risk of providing company expenses to a government employee or a high-risk individual.
- Pattern Recognition: Using Machine Learning and AI engine can detect patterns to detect employees who are repeat offenders or make accidental or opportunistic claims.
- Company-wide analytics: This sort of analytics can also allow your company to determine company-wide spend and audit trends providing real-time alerts when an expense is flagged as high risk. Managers can sort and filter expenses between policies, cost centers, and departments, and drill down to the high-risk individuals/employees and the top expense areas.
There is a misnomer that predictive analytics can be used in isolation for fraud detection. A detail to note is that predictive analytics cannot be used in isolation for fraud detection.
For example, in the case of changes such as PDS2 (Payments Services Directive) that was applied across EU member states, lack of historical data would starve predictive analytics of training data rendering it ineffective in the short term. In such cases, the risk can be mitigated through the use of a hybrid detection methodology, involving the use of business scenarios and the detection of anomalies through the use of experienced peer groups.
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