Mapping the trends of Financial Statement Fraud detection research from the historical roots and seminal work

Abstract

This research aims to identify the historical roots of Financial Statement Fraud (FSF) detection research and ascertain the trajectory of current and upcoming research in this field. This study conducted descriptive, reference spectroscopy, and scientific mapping analyses. To unearth the historical foundations of FSF detection research, the study employed the “Reference Publication Year Spectroscopy (RPYS)” technique. The study chose publications from 1989 to 2022 and identified a slow initial publication pace from 1989, followed by a surge in 2003, aligned with global accounting fraud scandals. Through RPYS, it identified 24 seminal research works (from 1881 to 2022) across multiple disciplines (mathematics, psychology, criminology, sociology, economics, finance, accounting, auditing, data analytics and machine learning) that contributed to the development of the FSF detection research. The trend of FSF research shifted from “financial reporting” to “machine learning”, which underscores the necessity for researchers, organizations, and policymakers to integrate emerging technologies like machine learning and data analytics and promote interdisciplinary and international cooperation to enhance the detection of FSF.