How to use machine learning to detect expense fraud

Expense fraud is a staggering cost for large enterprises. According to the ACFE Global Fraud Study, 5% of an organization's annual revenue is lost to expense fraud, most commonly seen as of inflating business reimbursements. It is a significant challenge for companies to detect and prevent this wastage effectively, and it is traditionally done manually under the watchful eyes of the finance department. 

In the last few years, the finance and banking industries have regarded machine learning as particularly helpful in detecting financial fraud. Manual processing has various shortcomings in speed and accuracy when dealing with a large amount of data, and ML algorithms can be a boon for any large organization. 

Let's discuss how companies can go about implementing this technology.

Creating a Well Articulated Expense Policy

The first step for companies in combating expense fraud is to create a non-conflicting expense policy. A well-structured policy should clearly outline the acceptable expenses and provide guidelines on documentation, approval processes, and reimbursement timelines. Every employee should have complete clarity about the permitted expenses according to their eligibility. By creating a comprehensive and transparent policy, companies can minimize the opportunities for fraudulent claims to slip through the cracks. 

This might seem like a no-brainer, but it is essential to check your organization's policy for any conflicting clauses or lack of detailed rules for specific scenarios to avoid the possibility of conflict. 

Eventually, the algorithms will be trained on the policy in place. So your ML will only be as effective as your policy is comprehensive and well-articulated because any conflicts in the policy will lead to inefficient algorithms. 

The Power of Machine Learning in Fraud Detection

Machine learning algorithms are exceptionally suited for detecting fraud because they can analyze large volumes of data in a short period of time. They have the capacity to gather and analyze data in real time and detect fraud in a matter of seconds. Moreover, ML models are easy to scale, making them a powerful tool for organizations of all sizes.

How Machine Learning Detects Expense Fraud

• Data Collection and Segmentation: The first step in the fraud detection procedure is to collect and segment data using machine learning technologies. This process involves gathering expense data from various sources, such as expense reports, invoices, and receipts. Once the data is collected, it is segmented into different categories: employee, department, expense type, and date.
• Training the Algorithm: After data segmentation, the machine learning model is given training algorithms to estimate the likelihood of fraud. This process involves feeding the model with historical expense data, including both legitimate and fraudulent transactions. The ML algorithm can learn to identify potential fraud indicators by analyzing patterns and relationships within the data.
• Implementing the Fraud Detection Model: Once the algorithm is trained on the given data, theorganization will obtain a fraud detection machine learning model. This model can be integrated into your existing expense management system to detect real-time suspicious transactions. The ML model can flag potentially fraudulent expenses for further review, helping your organization take swift action against fraud.

Utilizing SaaS Solutions for Fraud Detection

For enterprises keen on reducing employee expense fraud, software-as-a-Service (SaaS) solutions that explicitly incorporate machine learning algorithms trained for this purpose are available. These platforms offer various features, such as automated expense categorization, policy enforcement, and real-time fraud detection. By utilizing these solutions, companies can save time and resources by not having to develop their own ML models from scratch.

In conclusion, machine learning can potentially revolutionize expense fraud detection in organizations. By employing ML algorithms to analyze vast amounts of data, companies can identify and prevent fraudulent expense claims more effectively than traditional methods. To maximize the benefits of machine learning in detecting expense fraud, organizations should first establish a comprehensive expense policy and then explore the available SaaS solutions to find the right platform that fits their needs. Through these steps, companies can protect their bottom line and maintain a culture of integrity and transparency within their workforce.

The article has been written Ramesh Iyer, President and CRO, Happay