Virginia Cerullo

Papers

Total Citations

H-Index

About

Virginia Cerullo is a pioneering researcher at the intersection of artificial intelligence and financial auditing, best known for her groundbreaking work on applying neural networks to the detection of financial reporting fraud. Her most influential contributions emerged in 1999 through a two-part series exploring how AI systems — particularly neural networks — can be harnessed to identify fraudulent financial reporting, a challenge of profound importance to auditors, regulators, and investors alike. These papers, which together have accumulated nearly 70 citations, helped lay the conceptual groundwork for using machine learning tools in forensic accounting and audit risk assessment at a time when such applications were still largely novel. Cerullo's work demonstrated that AI technologies, which mimic human reasoning, learning, and problem-solving capabilities, could be effectively adapted to recognize patterns indicative of financial manipulation — offering a more systematic and scalable alternative to traditional audit approaches. By bridging computer science and accounting, she helped open an interdisciplinary dialogue that continues to resonate in today's data-driven auditing environment. Her research remains a foundational reference for students and practitioners exploring the role of emerging technologies in enhancing financial integrity and corporate accountability.