Fraud detection by machine learning techniques
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Institute of Engineering Mathematics, Universiti Malaysia Perlis
2023
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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 |
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Universiti Malaysia Perlis |
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http://dspace.unimap.edu.my/ |
| language |
English |
| topic |
Algorithms Fraud detection Feature selection Machine learning |
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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 |