[Video] Best Practice Tips For ERP Data Monitoring

Whatever the size and nature of your organization, you’re not immune to the consequences of fraudulent activity. Fraud can have a negative financial impact on your organization. It can diminish the trust of your current and future customers. Unfortunately, fraud can go undetected for months. Just because you haven’t discovered it yet doesn’t mean it’s not happening to you. ERP data monitoring increases your chances of detecting fraud early.

In this episode of the Appsian Insights video series, we’re going to look at the importance of monitoring access to critical or sensitive ERP data, the key objectives when it comes to monitoring, and how to get started.

A Key Step To Identifying Fraud Is ERP Data Monitoring

The key to successful fraud detection is by tracking changes to your critical ERP data, such as master data, bank accounts, or accounting configuration settings, with full details of who changed what and when, and automated alerts to notify you instantly of significant changes.

8 Best Practices For Developing A Fraud Monitoring Practice

Fraudulent activity can stay undiscovered in ERP applications because interpreting between authorized and unauthorized user activity is challenging. Here are some best practice tips for developing an efficient and sustainable fraud monitoring practice.

• Define the objective of the monitoring. Is it part of a risk management strategy or a standalone function?
• Assign responsibilities for areas such as people, processes, and technology.
• Identify and define the specific fraud you are trying to detect.
• Identify the potential fraud risk, including processes, data requirements, and logic.
• Develop and tests and alerts
• Review the output and evaluate the effectiveness
• Establish workflows and responsibilities for ongoing monitoring
• Implement and document the related processes.

Automation is a crucial part of successful ERP data monitoring and fraud prevention. Contact to learn how we can help your organization take the appropriate steps to prevent and detect fraudulent activity through data monitoring.