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Machine Learning Applications for Accounting Disclosure and Fraud Detection

dc.contributor.authorPassas, Ioannis N.
dc.contributor.authorGarefalakis, Alexandros
dc.contributor.authorLemonakis, Christos
dc.contributor.authorZopounidis, Constantin
dc.contributor.authorChimonaki, Christianna
dc.date.accessioned2022-01-22T09:41:58Z
dc.date.available2022-01-22T09:41:58Z
dc.date.issued2020-10
dc.identifier.isbn9781799848059
dc.identifier.urihttp://hdl.handle.net/11728/12120
dc.description.abstractDescription: The prediction of the valuation of the “quality” of firm accounting disclosure is an emerging economic problem that has not been adequately analyzed in the relevant economic literature. While there are a plethora of machine learning methods and algorithms that have been implemented in recent years in the field of economics that aim at creating predictive models for detecting business failure, only a small amount of literature is provided towards the prediction of the “actual” financial performance of the business activity. Machine Learning Applications for Accounting Disclosure and Fraud Detection is a crucial reference work that uses machine learning techniques in accounting disclosure and identifies methodological aspects revealing the deployment of fraudulent behavior and fraud detection in the corporate environment. The book applies machine learning models to identify “quality” characteristics in corporate accounting disclosure, proposing specific tools for detecting core business fraud characteristics. Covering topics that include data mining; fraud governance, detection, and prevention; and internal auditing, this book is essential for accountants, auditors, managers, fraud detection experts, forensic accountants, financial accountants, IT specialists, corporate finance experts, business analysts, academicians, researchers, and students. Coverage: The many academic areas covered in this publication include, but are not limited to: Data Mining Financial Management Fraud Detection Fraud Governance Fraud Prevention Internal Auditing Investment Analysis Machine Learning Risk Management Supervised Learningen_UK
dc.language.isoen_USen_UK
dc.publisherIGI Globalen_UK
dc.rights© 2021en_UK
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_UK
dc.source.urihttps://www.igi-global.com/gateway/book/244580en_UK
dc.subjectResearch Subject Categories::SOCIAL SCIENCES::Statistics, computer and systems scienceen_UK
dc.subjectResearch Subject Categories::SOCIAL SCIENCES::Business and economicsen_UK
dc.subjectBusiness and Managementen_UK
dc.subjectData Miningen_UK
dc.subjectFinancial Managementen_UK
dc.subjectFraud Detectionen_UK
dc.subjectFraud Governanceen_UK
dc.subjectInternal Auditingen_UK
dc.subjectMachine Learningen_UK
dc.subjectRisk Managementen_UK
dc.titleMachine Learning Applications for Accounting Disclosure and Fraud Detectionen_UK
dc.typeBooken_UK
dc.doi10.4018/978-1-7998-4805-9en_UK


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