Assessing Managers Fraud Through Analysis of Board of Directors Report by Data Mining

Detecting, evaluating and understanding fraud reports, called as misstated reports, has a long history in the accounting and financial literature. Shareholders choose managers as their agents in the company, so it is usual for them to be sensitive to the honesty of managers’ reports. Also auditors i...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Alireza Rahrovi Dastjerdi, Dariosh Foroghi (Ph.D), Gholamhosein Kiani (Ph.D)
Formato:	article
Lenguaje:	FA
Publicado:	Shahid Bahonar University of Kerman 2018
Materias:	fraud risk fraudulent reporting data mining lasso regressions Accounting. Bookkeeping HF5601-5689
Acceso en línea:	https://doaj.org/article/4282ca380d284b5dbc5c50f4124472d3
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4282ca380d284b5dbc5c50f4124472d3
record_format	dspace
spelling	oai:doaj.org-article:4282ca380d284b5dbc5c50f4124472d32021-11-04T19:53:43ZAssessing Managers Fraud Through Analysis of Board of Directors Report by Data Mining2008-89142476-292X10.22103/jak.2018.10913.2495https://doaj.org/article/4282ca380d284b5dbc5c50f4124472d32018-04-01T00:00:00Zhttps://jak.uk.ac.ir/article_1932_95f0e4efd0f606d9679fddd2bf7f2a8d.pdfhttps://doaj.org/toc/2008-8914https://doaj.org/toc/2476-292XDetecting, evaluating and understanding fraud reports, called as misstated reports, has a long history in the accounting and financial literature. Shareholders choose managers as their agents in the company, so it is usual for them to be sensitive to the honesty of managers’ reports. Also auditors in their investigations are required to evaluate and measure the fraud risk in the methods implemented by the management of entity, and then, to estimate the parameters of audit tests, and their proposed auditing fees. This study has used non-financial approach to detecting and evaluating managers' fraud risk that is based on the analysis of the text of board of directors' report. In this method, the words of the board's reports were reviewed, and after some refinements, a model was presented for evaluating high fraud risk index in companies, using a certain type of regression model, called LASSO. This model is able to identify high fraud risk index in companies, correctly with precision of 89% to 91%.Alireza Rahrovi DastjerdiDariosh Foroghi (Ph.D)Gholamhosein Kiani (Ph.D)Shahid Bahonar University of Kermanarticlefraud riskfraudulent reportingdata mininglasso regressionsAccounting. BookkeepingHF5601-5689FAمجله دانش حسابداری, Vol 9, Iss 1, Pp 91-114 (2018)
institution	DOAJ
collection	DOAJ
language	FA
topic	fraud risk fraudulent reporting data mining lasso regressions Accounting. Bookkeeping HF5601-5689
spellingShingle	fraud risk fraudulent reporting data mining lasso regressions Accounting. Bookkeeping HF5601-5689 Alireza Rahrovi Dastjerdi Dariosh Foroghi (Ph.D) Gholamhosein Kiani (Ph.D) Assessing Managers Fraud Through Analysis of Board of Directors Report by Data Mining
description	Detecting, evaluating and understanding fraud reports, called as misstated reports, has a long history in the accounting and financial literature. Shareholders choose managers as their agents in the company, so it is usual for them to be sensitive to the honesty of managers’ reports. Also auditors in their investigations are required to evaluate and measure the fraud risk in the methods implemented by the management of entity, and then, to estimate the parameters of audit tests, and their proposed auditing fees. This study has used non-financial approach to detecting and evaluating managers' fraud risk that is based on the analysis of the text of board of directors' report. In this method, the words of the board's reports were reviewed, and after some refinements, a model was presented for evaluating high fraud risk index in companies, using a certain type of regression model, called LASSO. This model is able to identify high fraud risk index in companies, correctly with precision of 89% to 91%.
format	article
author	Alireza Rahrovi Dastjerdi Dariosh Foroghi (Ph.D) Gholamhosein Kiani (Ph.D)
author_facet	Alireza Rahrovi Dastjerdi Dariosh Foroghi (Ph.D) Gholamhosein Kiani (Ph.D)
author_sort	Alireza Rahrovi Dastjerdi
title	Assessing Managers Fraud Through Analysis of Board of Directors Report by Data Mining
title_short	Assessing Managers Fraud Through Analysis of Board of Directors Report by Data Mining
title_full	Assessing Managers Fraud Through Analysis of Board of Directors Report by Data Mining
title_fullStr	Assessing Managers Fraud Through Analysis of Board of Directors Report by Data Mining
title_full_unstemmed	Assessing Managers Fraud Through Analysis of Board of Directors Report by Data Mining
title_sort	assessing managers fraud through analysis of board of directors report by data mining
publisher	Shahid Bahonar University of Kerman
publishDate	2018
url	https://doaj.org/article/4282ca380d284b5dbc5c50f4124472d3
work_keys_str_mv	AT alirezarahrovidastjerdi assessingmanagersfraudthroughanalysisofboardofdirectorsreportbydatamining AT darioshforoghiphd assessingmanagersfraudthroughanalysisofboardofdirectorsreportbydatamining AT gholamhoseinkianiphd assessingmanagersfraudthroughanalysisofboardofdirectorsreportbydatamining
_version_	1718444586682023936

Assessing Managers Fraud Through Analysis of Board of Directors Report by Data Mining

Ejemplares similares