Hardcover. Condizione: Very Good. Light shelfwear. Pages clean and intact.
Lingua: Inglese
Editore: Business Science Reference, 2020
ISBN 10: 1799848051 ISBN 13: 9781799848059
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Lingua: Inglese
Editore: Business Science Reference, 2020
ISBN 10: 1799848051 ISBN 13: 9781799848059
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Lingua: Inglese
Editore: Business Science Reference, 2020
ISBN 10: 1799848051 ISBN 13: 9781799848059
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Lingua: Inglese
Editore: Business Science Reference, 2020
ISBN 10: 1799848051 ISBN 13: 9781799848059
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Lingua: Inglese
Editore: Business Science Reference, 2020
ISBN 10: 1799848051 ISBN 13: 9781799848059
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Lingua: Inglese
Editore: Business Science Reference, 2020
ISBN 10: 1799848051 ISBN 13: 9781799848059
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Lingua: Inglese
Editore: Business Science Reference, 2020
ISBN 10: 1799848051 ISBN 13: 9781799848059
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Uses machine learning techniques in accounting disclosure and identifies methodological aspects revealing the deployment of fraudulent behaviour and fraud detection in the corporate environment. The book applies machine learning models to identify quality.
Lingua: Inglese
Editore: Business Science Reference, 2020
ISBN 10: 1799848051 ISBN 13: 9781799848059
Da: preigu, Osnabrück, Germania
EUR 273,25
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Aggiungi al carrelloBuch. Condizione: Neu. Machine Learning Applications for Accounting Disclosure and Fraud Detection | Stylianos Papadakis (u. a.) | Buch | Gebunden | Englisch | 2020 | Business Science Reference | EAN 9781799848059 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Lingua: Inglese
Editore: Business Science Reference, 2020
ISBN 10: 1799848051 ISBN 13: 9781799848059
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 328,55
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Aggiungi al carrelloBuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.