Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Divya, D.; Bhasi, Vivek M.; Arunkumar, O.N. Title A name given to the resource Detection of earnings manipulation in financial reports: a data-driven approach Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Accounting, Auditing and Performance Evaluation;Volume;21;Issue;2026-04-03 00:00:00;pp.499-512 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJAAPE.2025.148318" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJAAPE.2025.148318</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015465553?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015465553?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Divya D., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Bhasi V.M., Department of Computer Science Engineering, The Pennsylvania State University, 16801, PA, United States; Arunkumar O.N., Symbiosis Institute of Business Management (SIBM), Symbiosis International (Deemed University) (SIU), Bengaluru, 560100, India Description An account of the resource Earnings manipulation attracts attention from both industry and academia as detection of earnings manipulation helps them to invest wisely rather than based on falsified financial statements. This paper discusses a process through which earnings manipulators can be identified. This can help shareholders to detect companies who made modifications to their financial statements. Earlier researchers developed many mathematical models to identify earnings manipulators. However, their works require an in-depth understanding of financial ratios and accounting principles. The advancement of data-driven algorithms has now brought comparable machine learning detection techniques into the picture, wherein data scientists can use historical data to detect earnings manipulators. This paper discusses the use of an artificial neural network (ANN) for detecting manipulations in the dataset. Copyright 2025 Inderscience Enterprises Ltd. Subject The topic of the resource data analytics; earnings manipulation; financial ratio; financial report; fraud; neural network Publisher An entity responsible for making the resource available Inderscience Publishers Relation A related resource ISSN: 17408008; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online