E3S Web of Conf.
Volume 389, 2023Ural Environmental Science Forum “Sustainable Development of Industrial Region” (UESF-2023)
|Number of page(s)||19|
|Section||IT in Environmental Science|
|Published online||31 May 2023|
The application of artificial intelligence techniques in credit card fraud detection: a quantitative study
Asia Pacific University, Jalan Teknologi 5, Taman Teknologi Malaysia, 57000 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia
* Corresponding author: email@example.com
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.
Key words: Identity Theft / Credit Card Fraud Detection / Artificial Intelligence / Machine Learning / Data Mining / Fuzzy Logic
© The Authors, published by EDP Sciences, 2023
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