Due to the unique and creative ways employees “game” the system with purchasing cards and expense reimbursements, machine learning is also useful in identifying previously unknown schemes in these areas. These activities can create unusual combinations of debt and/or credit.

Case experience has shown machine learning to be effective in identifying purchasing behavior such as frequent vendors, unusually consistent amounts, certain transactions that occur in tandem with each other and items occurring with unusual regularity. Similarly, expense reimbursements may be flagged as unusual by a machine learning based system that may not conform to typical rules-based observations such as rounded amounts, “just under” threshold amounts and the traditional Benford’s Law analysis.

It basically boils down to the fact that we (humans) cannot analyze the entire file and find trends in that data. Machine learning in the MindBridge Ai-Auditor can decompile the entire structure, observe trends, identifies unusual combinations and present that information. These are anomalies that are ‘unusual’, and which the auditor might not have thought about.