FREMONT, CA – Procurement fraud has always been a sore subject in organizations. Although it is not practical to doubt employees and suppliers, companies need to take precautionary steps. Procurement fraud not only affects the targeted organizations but almost everyone involved in the process. Also, the damage incurred by the organization, both in terms of finance and reputation, might be irrevocable.

Procurement fraud is not limited to a few organizations. SAS research shows that almost 31 percent of businesses in the UK have fallen victims to contract bid rigging, and over 43 percent have been given fraudulent invoices. Besides, the PwC has ranked procurement fraud as the second most prevalent economic crime since 2014.

If not taken care of at the earlier stages, procurement fraud can drag an organization to the ground. In this regard, many organizations have come up with practical approaches to prevent procurement fraud. Underestimating the impact of procurement fraud is one of the main reasons why companies fail to identify the risk. Hence, it is imperative for organizations to take necessary measures to thwart procurement fraud at its nascent stages.

Organizations need to acknowledge the importance of implementing long term safety measures as they are vulnerable to procurement fraud through every phase of the process. It is necessary to achieve a monitoring and control system to track the procure-to-pay chain, including regular audits.

Lack of awareness and communication can significantly hamper the capability of an organization to deal with procurement fraud. Since it is challenging to track procurement fraud within an organization, it is vital for every employee to be involved. Hence, organizations have to facilitate effective collaboration and sharing of data.

Organizations need to adopt a comprehensive analytics approach and enforce business rules during procurement. Employing anomaly detection can enable organizations to keep track of unusual behavior. Based on the anomalies, the software draws fraud risk scores.

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The analytics approach leverage machine learning to balance the behavioral anomaly detection. Even if one link analysis shows a relationship between different parties, the anomaly detection can cross check the transactions by comparing it with others of its kind. As a result, there is minimal risk of false positives.