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AI Analytics Centre Stage at the ‘Who’s Who of Audit Data Analytics’

Michael P. Cangemi, a former CAE, CFO & CEO, is a Senior Fellow and Advisory Board Member of the Rutgers Continuous Auditing and Reporting Lab. 

 

I am often asked why I have devoted my practice to the expanded use of data analytics for the last 10 years. During that time, I’ve published three research papers; authored numerous articles; and given many interviews, speeches, and webinars, all focused on how to use technology to make your business more effective and improve GRC through the use of data analytics and other fintech. 

 

A core theme of my career has been tremendous value creation, enhanced internal controls, and business process improvement from the use of automation and specifically monitoring systems and data analytics. With the ongoing explosion of data availability, it seemed like the time was right to focus on the potential of audit data analytics, and I knew I would enjoy helping others follow the same path.

 

Automated auditing goes back to the 1970s

 

Another reason resulted from meeting Professor Miklos Vasarhelyi, who invited me to speak to his students on my work with automated auditing in the 1970s. When he joined Rutgers University as KPMG Distinguished Professor of Accounting Information Systems, he asked me to join the Advisory Board of his newly formed Rutgers Continuous Auditing & Reporting (CAR) LAB.

 

A CAR Symposium was soon added and the who’s who of the continuous auditing community have been meeting ever since. The 47th World Continuous Auditing and Reporting Symposium (WCARS) was held at the Rutgers Business School in New Jersey this past November, as is now the custom every year. Most years, additional WCARSs are held in other locations all over the world and are hosted by universities with a similar focus. 

 

Through these programs, numerous Ph.Ds., students, and audit professionals have enhanced their focus on my career core theme, “value creation, enhanced internal controls, and business process improvement from the use of automation and specifically monitoring systems and data analytics.” So, it was natural that, late in my career, after moving up from CAE to CIO, CFO, CEO, I would focus on an area of such great interest and value. 

 

The CAR LAB’s advice is sought after by professional associations including the deeply involved AICPA and regulators such as the SEC and PCAOB. There are now similar academic groups worldwide including in Canada, at the University of Waterloo, where a similar annual conference is held. The success is enhanced by the involvement and support of analytics software companies, and CaseWare IDEA is a leader among them. 

 

As a key sponsor of the Rutgers CAR Lab, CaseWare led off this year’s WCAR Symposium with a presentation on their new AnalyticsAI offering. AnalyticsAI is an AI-based advanced analytics solution poised to revolutionize the audit industry. AnalyticsAI promises to grant auditors and financial professionals unprecedented easy access to advanced data analytics critical to the data-driven audit.

 

The CaseWare IDEA presentation on AnalyticsAI was noteworthy for a number of reasons. First, it operates in the cloud and second, the advancement in and focus on scoring exceptions. One of the major hurdles for using analytics has been the number of and need to understand findings. AnalyticsAI addresses this issue by sorting findings into categories such as high-risk users and high-risk Journal Entries. 

 

This approach is consistent with the AICPA RADAR project discussion at the symposium and the announcement of planned non-authoritative guidance. The AICPA plans to publish a risk assessment tool, as a model for filtering and categorizing findings or exceptions. They used the term “notable items”.

 

AI audit analytics will lead the charge on value creation for internal audit

 

IDEA bills AnalyticsAI as an AI-based advanced analytics solution that will revolutionize the audit industry by granting auditors and financial professionals unprecedented access to advanced data analytics critical to the data-driven audit. The basic concept is that it uses artificial intelligence-based analysis to improve efficiency, quality, and value by analyzing all data at once.

 

“We’re bringing this groundbreaking technology to market to address a new reality: conventional auditing processes simply cannot keep pace with the volume of data generated in today’s digital business environment,” said Matt Dodds, CaseWare IDEA General Manager, emphasizing that until now, similar advanced analytical capabilities were only available to data specialists.

 

This new offering confirms my belief that the use of automated auditing with monitoring and analytics results in value creation, enhanced internal controls and business process improvement from the use of automation and specifically monitoring systems and data analytics. It is also a giant step toward changing the backwardly looking audit model and addressing 100% population audits. These developments are fascinating and are making my highest aspirations for the field of audit automation and data analytics a reality.

 

Note: The opinions expressed in this article are solely those of the author.

 

About Michael P. Cangemi: Michael P. Cangemi is a senior fellow at Rutgers University and serves on the Rutgers Continuous Auditing and Reporting Lab Advisory Board. A former CFO and CEO, a prolific writer, active speaker and senior advisor to various companies, Mr. Cangemi now focuses on providing continuous auditing, continuous monitoring and analytics intelligence for GRC, Finance and Business Process Improvements. He serves on FEI’s Committee on Finance & Technology (CFIT) and their GRC Sub Committee; the EDPACS Editorial Advisory Board; and the Lukka Audit Advisory Board focused on auditing blockchain.  His book Managing the Audit Function 3rd Edition was published by Wiley and has been translated into Chinese and Serbian.

 


 

Click to watch the video: How an IT auditor transformed their role and ultimately the organization with the use of IDEA data analytics.