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Machine Learning Applications for Accounting Disclosure and Fraud Detection
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Author(s)/Editor(s): Stylianos Papadakis (Hellenic Mediterranean University, Greece), Alexandros Garefalakis (Hellenic Mediterranean University, Greece), Christos Lemonakis (Hellenic Mediterranean University, Greece), Christiana Chimonaki (University οf Portsmouth, UK)and Constantin Zopounidis (School of Production Engineering and Management, Technical University of Crete, Greece & Audencia Business School, France)
Copyright: ©2021
DOI: 10.4018/978-1-7998-4805-9
ISBN13: 9781799848059
ISBN10: 1799848051
EISBN13: 9781799848066
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DescriptionThe 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.
Reviews and Testimonials
This book contributes systematically to the development of modern financial research using machine learning methods to identify cases of falsification of financial statements. The main contribution is undoubtedly in the way and the philosophy that the management of a company can operate, determining to a large extent how to organize the company as an entity. Besides, with the use of new machine learning techniques, the work becomes more systematic and supportive for a wide range of analysts, investors, students, and auditing professionals who wish to adopt new techniques to identify falling data and data in corporate Fraud.
Therefore, the book tries to create that framework for a systematic and methodological approach to corporate financial statements.
– Ioannis Passas, Researcher, GreeceDepartment of Business Administration & Tourism, Hellenic Mediterranean University, and Professor,
Author's/Editor's Biography
Alexandros Garefalakis (Ed.)
Alexandros Garefalakis is Certified Public Accountant (Fellow of CPA), Certified Management Accountant (CMA) and he is an Assistant Professor at the Dept. of Business Administration and Tourism at Hellenic Mediterranean University (HMU) in Greece. Also, he has co-authored 7 books on Audit Accounting, Financial Accounting, Management Accounting and Research Methods for Business issues and his areas of research interest include the Disclosure Narrative information, ESG, I.F.R.S, Management Commentary Index (Ma.Co.I), Auditing, Quality of Financial Statements, Weighting models in Accounting, Accounting and Operation Research, Multiple Criteria Decision‐Making in Management Accounting.
Christos Lemonakis (Ed.)
Christos Lemonakis is an Assistant Professor of Business Administration on SMEs Management at the Hellenic Mediterranean University, Department of Management Science and Technology (Agios Nikolaos, Crete, Greece). His research interests are Cost Accounting and Responsible Management Education, Corporate Governance, and Entities Sustainability.
Constantin Zopounidis (Ed.)
Constantin Zopounidis has been elected to the prestigious The Real Academia de Ciencias Económicas y Financieras (RACEF). He joins the ranks of Daniel Kahneman (Nobel Laureate), Romano Prodi, and Joseph E. Stiglitz (Nobel Laureate) who belong to this Institution. It is with pride and delight that we communicate that Professor Constantin Zopounidis, Editor-in-Chief of Operational Research and Editor of the renowned Handbook of Multicriteria Analysis, has been elected to the RACEF – Royal Academy of Economic and Finance Sciences of Spain. The Royal Academy of Economic and Finance Sciences of Spain was officially established in Spain, in 1958, but its roots can be traced back to the 18th century when King Ferdinand VI of Spain set up the Royal Private Board of Commerce. RACEF promotes the cooperation between researchers of the most important institutions and academies around the world, and seeks to advance the scientific knowledge and the existing decision and policy making practices in the fields of economics and finance. The members of RACEF include prestigious researchers, senior policy makers, and top executives from all over the world. Among others, the Academicians of RACEF include Valéry Giscard d'Estaing (former President of France), Romano Prodi (former President of Italy and the European Commission), José Ángel Gurría (Secretary General of OECD), as well as Nobel Prize laureates Robert Aumann, Daniel Kahneman, Finn Kydland, Eric Maskin, and Joseph Stiglitz ( https://www.racef.es/). Also, Professor Zopounidis has been elected as Academician, in the Royal Academy of Doctors (Spain, June 2014). As and Academician, Constantin was elected to the Executive Committee of the International Society on Multiple Criteria Decision Making following a vote in which 677 people participated from all over the world. This Society brings together 2200 people from 100 countries. The term of office shall be for four years from 2015 to 2019. Professor C. Zopounidis was also honored by the same Society with Edgeworth - Pareto Award in 2013.
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