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Fraud detection by machine learning techniques

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Main Authors: Khai, Wah Khaw, Esraa Faisal, Malik, Xin, Ying Chew
Other Authors: khaiwah@usm.my
Format: Article
Language:English
Published: Institute of Engineering Mathematics, Universiti Malaysia Perlis 2023
Subjects:
Algorithms
Fraud detection
Feature selection
Machine learning
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77719
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spelling my.unimap-777192023-01-25T04:06:29Z Fraud detection by machine learning techniques Khai, Wah Khaw Esraa Faisal, Malik Xin, Ying Chew khaiwah@usm.my School of Management, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia Algorithms Fraud detection Feature selection Machine learning Link to publisher's homepage at https://amci.unimap.edu.my/ Over the years, the negative impact of financial fraud on organizations and countries have been increasing significantly. Conventional methods such as expert’s judgment are usually used to detect financial fraud. However, these methods suffer from serious drawbacks due to time consumption, human errors and high operational cost. Hence, the need for automating the fraud detection method arises. Researchers have been using machine learning to detect fraudulent cases, this approach has been widely used in financial fraud detection to address the shortcomings of conventional methods as it automates the detection process and has the potential to resolve the disparity between the impact of fraud detection and its efficacy. This research applies two feature selection methods (correlation and wrapper) with three different machine learning algorithms (Naïve Bayes, Support Vector Machine and Random Forest) to determine the optimum algorithm that can efficiently classify fraudulent and non-fraudulent firms based on a real-life dataset from the Auditor General Office of India between 2015 and 2016. A data science life cycle in machine learning was adopted. The results and evaluation revealed that machine learning algorithms had a superior advantage and can be used over the traditional methods of fraud detection. 2023-01-25T04:06:29Z 2023-01-25T04:06:29Z 2022-12 Article Applied Mathematics and Computational Intelligence (AMCI), vol.11(1), 2022, pages 88-103 2289-1315 (print) 2289-1323 (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77719 en Institute of Engineering Mathematics, Universiti Malaysia Perlis
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Algorithms
Fraud detection
Feature selection
Machine learning
spellingShingle Algorithms
Fraud detection
Feature selection
Machine learning
Khai, Wah Khaw
Esraa Faisal, Malik
Xin, Ying Chew
Fraud detection by machine learning techniques
description Link to publisher's homepage at https://amci.unimap.edu.my/
author2 khaiwah@usm.my
author_facet khaiwah@usm.my
Khai, Wah Khaw
Esraa Faisal, Malik
Xin, Ying Chew
format Article
author Khai, Wah Khaw
Esraa Faisal, Malik
Xin, Ying Chew
author_sort Khai, Wah Khaw
title Fraud detection by machine learning techniques
title_short Fraud detection by machine learning techniques
title_full Fraud detection by machine learning techniques
title_fullStr Fraud detection by machine learning techniques
title_full_unstemmed Fraud detection by machine learning techniques
title_sort fraud detection by machine learning techniques
publisher Institute of Engineering Mathematics, Universiti Malaysia Perlis
publishDate 2023
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77719
_version_ 1772813100750209024
score 13.214268

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