The application of artificial intelligence techniques in credit card fraud detection: a quantitative study
Credit card fraud is a major problem that has caused several challenges for practitioners in the accounting and finance industry due to a large number of daily transactions as well as the difficulties encountered in identifying fraudulent transactions. The purpose of this study is to investigate the...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/26/e3sconf_uesf2023_07023.pdf |
| _version_ | 1797807938218229760 |
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| author | Dayyabu Yusuf Yusuf Arumugam Dhamayanthi Balasingam Suresh |
| author_facet | Dayyabu Yusuf Yusuf Arumugam Dhamayanthi Balasingam Suresh |
| author_sort | Dayyabu Yusuf Yusuf |
| collection | DOAJ |
| description | Credit card fraud is a major problem that has caused several challenges for practitioners in the accounting and finance industry due to a large number of daily transactions as well as the difficulties encountered in identifying fraudulent transactions. The purpose of this study is to investigate the application of artificial intelligence techniques as a fraud detection mechanism that can effectively and efficiently detect credit card fraud and identify fraudulent financial transactions. The data was acquired from 100 respondents across the accounting and finance industry and analysed using SPSS. Researcher analysed the data using regression analysis, Pearson correlation coefficient, and reliability analysis. Findings revealed that the three artificial intelligence techniques machine learning, data mining, and fuzzy logic have a significant positive relationship with credit card fraud detection. However, fuzzy logic was discovered to be the least utilized by experts due to its low accuracy/precision in comparison with machine learning and data mining. Based on these findings, our study concludes that the application of artificial intelligence techniques provides experts with better accuracy and efficiency in detecting fraudulent transactions. Therefore, it is recommended that fraud examiners, auditors, accountants, bankers, and organizations should implement and apply artificial intelligence techniques in order to spot anomalies faster and identify fraudulent financial transactions effectively and efficiently. |
| first_indexed | 2024-03-13T06:30:08Z |
| format | Article |
| id | doaj.art-9fb902584f814ab2a1c3400eecafbd1b |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-13T06:30:08Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-9fb902584f814ab2a1c3400eecafbd1b2023-06-09T09:08:58ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013890702310.1051/e3sconf/202338907023e3sconf_uesf2023_07023The application of artificial intelligence techniques in credit card fraud detection: a quantitative studyDayyabu Yusuf Yusuf0Arumugam Dhamayanthi1Balasingam Suresh2Asia Pacific UniversityAsia Pacific UniversityAsia Pacific UniversityCredit card fraud is a major problem that has caused several challenges for practitioners in the accounting and finance industry due to a large number of daily transactions as well as the difficulties encountered in identifying fraudulent transactions. The purpose of this study is to investigate the application of artificial intelligence techniques as a fraud detection mechanism that can effectively and efficiently detect credit card fraud and identify fraudulent financial transactions. The data was acquired from 100 respondents across the accounting and finance industry and analysed using SPSS. Researcher analysed the data using regression analysis, Pearson correlation coefficient, and reliability analysis. Findings revealed that the three artificial intelligence techniques machine learning, data mining, and fuzzy logic have a significant positive relationship with credit card fraud detection. However, fuzzy logic was discovered to be the least utilized by experts due to its low accuracy/precision in comparison with machine learning and data mining. Based on these findings, our study concludes that the application of artificial intelligence techniques provides experts with better accuracy and efficiency in detecting fraudulent transactions. Therefore, it is recommended that fraud examiners, auditors, accountants, bankers, and organizations should implement and apply artificial intelligence techniques in order to spot anomalies faster and identify fraudulent financial transactions effectively and efficiently.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/26/e3sconf_uesf2023_07023.pdfidentity theftcredit card fraud detectionartificial intelligencemachine learningdata miningfuzzy logic |
| spellingShingle | Dayyabu Yusuf Yusuf Arumugam Dhamayanthi Balasingam Suresh The application of artificial intelligence techniques in credit card fraud detection: a quantitative study E3S Web of Conferences identity theft credit card fraud detection artificial intelligence machine learning data mining fuzzy logic |
| title | The application of artificial intelligence techniques in credit card fraud detection: a quantitative study |
| title_full | The application of artificial intelligence techniques in credit card fraud detection: a quantitative study |
| title_fullStr | The application of artificial intelligence techniques in credit card fraud detection: a quantitative study |
| title_full_unstemmed | The application of artificial intelligence techniques in credit card fraud detection: a quantitative study |
| title_short | The application of artificial intelligence techniques in credit card fraud detection: a quantitative study |
| title_sort | application of artificial intelligence techniques in credit card fraud detection a quantitative study |
| topic | identity theft credit card fraud detection artificial intelligence machine learning data mining fuzzy logic |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/26/e3sconf_uesf2023_07023.pdf |
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