Michael J. Cerullo

Papers

Total Citations

H-Index

About

Michael J. Cerullo is a researcher whose work sits at the intersection of artificial intelligence and accounting, with a particular focus on applying machine learning techniques to financial fraud detection. His most recognized contributions come from a two-part series published in 1999, exploring the use of neural networks to predict financial reporting fraud — a pioneering effort to harness AI's pattern-recognition capabilities for auditing and forensic accounting purposes. Together, these papers have accumulated 67 citations, reflecting meaningful influence within the specialized community of accounting information systems and AI-assisted auditing research. Cerullo's work arrived at a formative moment in the adoption of AI tools within business and finance, helping to establish a conceptual and methodological foundation for using intelligent systems — including natural language processing and machine learning — to detect irregularities in financial statements. By demonstrating that neural networks could serve as practical fraud-detection instruments, his research helped bridge the gap between computer science innovation and real-world accounting practice. For students and researchers exploring the role of AI in financial oversight, Cerullo's contributions represent an important early chapter in what has since become a rapidly expanding field of study.