With the progressive automation of business processing systems, their transparency for examination has been substantially reduced. Methods are needed to assure society that processes are adequate, anomalies are minimal, and cyber threats are under control. Prof. Miklos Vasarhelyi, KPMG Distinguished Professor of Accounting Information Systems at Rutgers University, brings modern analytic and IT technology to auditing and monitoring to assure that financial statements and systems in companies are fairly represented. By automating methods to perform auditing activities like control and risk assessments on a frequent basis through continuous auditing, Prof. Vasarhelyi provides key breakthroughs in keeping real-time auditing methods more efficient, transparent, and accurate, and works hard at facilitating organizations to adopt them.

Continuous auditing research was first developed by Prof. Vasarhelyi himself at the Bell Laboratories in 1986-1990 to assess the correctness of AT&T’s billing systems. At that time, auditing was still being developed as a manual process; continuous auditing was not recognized as important for about a decade. Prof. Vasarhelyi and his team, however, continued to develop methodologies for continuous and automated assurance of organizations for the past 20 years. Now, in the world of big data and highly integrated systems, continuous auditing will be the only feasible way to provide assurance and bring auditing to a more predictive and automated domain, and leading professional organizations in accounting have already published studies and guidelines for its use. Prof. Vasarhelyi is therefore a true pioneer in the field, chairing over 40 dissertations and working with KPMG, one of the Big Four auditors, the largest private bank in Brazil, and other organizations to provide analytics and statistics for business processing systems. Professor Vasarhelyi leads the CarLab (Continuous Audit and reporting Laboratory) with a group of about 35 researchers.

Current research includes:

• Continuous data level assurance (CDA) and exception reporting: The traditional form of continuous audit (Vasarhelyi & Halper, 1991) initially performed at AT&T by the Bell Laboratories compares expected models of performance to actual data extracted directly from corporate data stores. These are compared and if significant differences are found alarms are issued. This approach has been used for fraud and anomaly detection. More recent work develops sophisticated data-based normative models for standards of performance and even “predictive” models for filtering transactions prior to execution (preventive audit).
• Continuous control monitoring (CCM) in ERPs (e.g. SAP and Oracle), and calling attention to exceptions: Based on work performed with Siemens a control baseline comparison methodology has been developed. Dr. Vasarhelyi’s current work manages to automate 63% of the auditor actions in an audit action sheet. There is a tremendous need for increasing this percentage by automating more functions and reducing the scope of tests, as the control monitoring is daily.
• Continuous risk monitoring and assessment (CRMA): This is the most incipient of the three areas. Proposed in 2010, only two experiments have been performed but shows tremendous potential in particular with the PCAOB pressure towards a risk based audit. It aims to monitor risks of the process in three dimensions: operations risks, environmental risks, and “black swans”. (Moon, 2015)