Forensic Accounting: Q&A with Matthias Demmer

Faculty member Matthias Demmer is internationally recognized as an expert in financial crime investigations

June 28, 2024
Matthias Demmer

Matthias Demmer

Matthias Demmer is a director in AlixPartners’ risk and technology practice, specializing in data analytics and artificial intelligence. He has over 15 years of hands-on problem-solving experience, often for multinational corporations and large financial institutions, and is internationally recognized as an expert in leading financial crime investigations.

In his role as adjunct professor in the Department of Accounting and Information Management at the University of Tennessee, Knoxville, Haslam College of Business, Demmer instructs Master of Accountancy (MAcc) students in methods for forensic accounting, data analytics and eDiscovery.

What is forensic accounting?

Forensic accounting, or financial forensics, is a specialized field that investigates financial data to uncover fraud or financial misconduct. Forensic accountants work closely with lawyers, internal and external auditors and even law enforcement during criminal investigations, litigations or corporate investigations. Forensic accounting has played a pivotal role in uncovering fraud cases such as Enron, the Bernie Madoff Ponzi scheme and the WorldCom accounting fraud. It is a very exciting profession.

How do forensic accountants use data analytics?

Data is the most valuable asset in forensic accounting investigations. Forensic accountants deploy data analytics to detect fraud patterns, analyze transactions, assess risks, examine electronic evidence and support litigation. Depending on the case, the investigative approach applied and the type of data involved, various methods and technologies are utilized. Data analytics improves the effectiveness and precision to reveal financial misconduct in today’s data-based environment.

How do you expect forensic data analytic processes to change in the next five years?

As the Nobel Prize in Physics winner Niels Bohr noted, predictions are very difficult [to make], especially about the future. However, certain trends are very likely to come. Analytical processes will become more automated and integrate more advanced technologies such as artificial intelligence (AI). Also, the collaboration and intersection with other data technologies such as data engineering and visualization is very likely.

What challenges do you foresee in integrating AI in forensic accounting practices?

AI technologies change fast, which makes predictions harder, but I think the most important challenge is to become a reliable technology with a [minimal] error margin. Forensic investigations do not allow even the smallest room for error. Investigations could lead to high fines, reputational damage or imprisonment. Also, law enforcement and other regulators only accept clear evidence. Generative AI, especially, needs to show that its analyses are always accurate and based on facts.

For UT Volunteers interested in the forensic analytics field, what type of training or education should they pursue?

To become a forensic analytics professional, I would recommend focusing on three main skills: a) subject matter expertise (e.g., accounting knowledge), b) data handling skills and c) experience with analytical tools and techniques. I think the UT MAcc program already provides very solid foundations for graduates to build on. I would also suggest learning how to work with relational and non-relational databases and some common programming languages (e.g., Python). After graduation, I would advise gaining some practical experience. Big accounting firms often offer entry-level positions in this field.


Stacy Estep, writer/publicist,