Beyond the Numbers: A Machine Learning-Powered Analysis of the Responsibilities, Qualifications, and Requirements of Forensic Accountants

ABSTRACT As global fraud increases, the need for forensic accountants is evident. To clarify contemporary employer demand, we analyze 238 job postings collected from 31 countries using a hybrid approach combining manual analysis of position qualifications with three machine learning-based natural language processing (NLP) techniques—term frequency (TF)-inverse document frequency (IDF), latent Dirichlet allocation (LDA), and named entity recognition (NER)—to analyze position responsibilities. Results indicate that credentials such as certified fraud examiner (CFE) and certified public accountant (CPA), possession of a bachelor’s degree, and prior professional experience are dominant and foundational requirements. Analytical software expertise is desirable, while nearly a fourth of positions include work travel. Soft communication skills are highly valued. Responsibilities highlight the dual nature of the role, which combines rigorous analytical work with client-facing communication tasks to effectively write, translate, and present reports; provide expert testimony; and assist clients with investigative, legal, and business development tasks. Data Availability: Data are available upon request. JEL Classifications: J010; J240; M400.