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A Big Data Approach to Accounting Fraud Detection Using Data Envelopment Analysis and One Class Support Vector Machine.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Big Data Approach to Accounting Fraud Detection Using Data Envelopment Analysis and One Class Support Vector Machine./
Author:	Wilfred, Kattren.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	73 p.
Notes:	Source: Masters Abstracts International, Volume: 83-01.
Contained By:	Masters Abstracts International83-01.
Subject:	Operations research. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28317760
ISBN:	9798522943912

A Big Data Approach to Accounting Fraud Detection Using Data Envelopment Analysis and One Class Support Vector Machine.
Wilfred, Kattren.

A Big Data Approach to Accounting Fraud Detection Using Data Envelopment Analysis and One Class Support Vector Machine. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 73 p.

Source: Masters Abstracts International, Volume: 83-01.

Thesis (M.A.S.)--University of Toronto (Canada), 2021.

This item must not be sold to any third party vendors.

This thesis investigates a method to use Data Envelopment Analysis (DEA) in the context of big data to improve data analysis for the volume and velocity aspects of big data. First, DEA is used to identify fraud indicators in a large dataset. One class support vector machine (OC-SVM) is then used to identify outliers in the same dataset to evaluate the performance of the proposed DEA model. A second DEA model is proposed as a variable selection tool to enhance the performance of an OC-SVM model. The results show that although the proposed DEA model on its own is not as effective in detecting anomalies as OC-SVM, there is potential to use DEA as a variable selection tool to enhance the training and prediction process for anomaly detection using OC-SVM. The parameters and methods examined in this thesis are not exhaustive, but it does provide a baseline for future work.

ISBN: 9798522943912Subjects--Topical Terms:

547123
Operations research.
Subjects--Index Terms:

Anomaly Detection

A Big Data Approach to Accounting Fraud Detection Using Data Envelopment Analysis and One Class Support Vector Machine.
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500 $a Source: Masters Abstracts International, Volume: 83-01.
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506 $a This item must not be sold to any third party vendors.
520 $a This thesis investigates a method to use Data Envelopment Analysis (DEA) in the context of big data to improve data analysis for the volume and velocity aspects of big data. First, DEA is used to identify fraud indicators in a large dataset. One class support vector machine (OC-SVM) is then used to identify outliers in the same dataset to evaluate the performance of the proposed DEA model. A second DEA model is proposed as a variable selection tool to enhance the performance of an OC-SVM model. The results show that although the proposed DEA model on its own is not as effective in detecting anomalies as OC-SVM, there is potential to use DEA as a variable selection tool to enhance the training and prediction process for anomaly detection using OC-SVM. The parameters and methods examined in this thesis are not exhaustive, but it does provide a baseline for future work.
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