Statistical Data-Generative Machine Learning-Based Credit Card Fraud Detection Systems
সংরক্ষণ করুন:
| প্রকাশিত: | Mathematics vol. 13, no. 15 (2025), p. 2446-2461 |
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| প্রধান লেখক: | |
| অন্যান্য লেখক: | |
| প্রকাশিত: |
MDPI AG
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| ট্যাগগুলো: |
কোনো ট্যাগ নেই, প্রথমজন হিসাবে ট্যাগ করুন!
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| 024 | 7 | |a 10.3390/math13152446 |2 doi | |
| 035 | |a 3239074511 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231533 |2 nlm | ||
| 100 | 1 | |a Feng Xiaomei | |
| 245 | 1 | |a Statistical Data-Generative Machine Learning-Based Credit Card Fraud Detection Systems | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study addresses the challenges of data imbalance and missing values in credit card transaction datasets by employing mode-based imputation and various machine learning models. We analyzed two distinct datasets: one consisting of European cardholders and the other from American Express, applying multiple machine learning algorithms, including Artificial Neural Networks, Convolutional Neural Networks, and Gradient Boosted Decision Trees, as well as others. Notably, the Gradient Boosted Decision Tree demonstrated superior predictive performance, with accuracy increasing by 4.53%, reaching 96.92% on the European cardholders dataset. Mode imputation significantly improved data quality, enabling stable and reliable analysis of merged datasets with up to 50% missing values. Hypothesis testing confirmed that the performance of the merged dataset was statistically significant compared to the original datasets. This study highlights the importance of robust data handling techniques in developing effective fraud detection systems, setting the stage for future research on combining different datasets and improving predictive accuracy in the financial sector. | |
| 610 | 4 | |a American Express Co | |
| 653 | |a Machine learning | ||
| 653 | |a Datasets | ||
| 653 | |a Hypothesis testing | ||
| 653 | |a Sampling techniques | ||
| 653 | |a Hypotheses | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Credit card fraud | ||
| 653 | |a Fraud prevention | ||
| 653 | |a Decision making | ||
| 653 | |a Missing data | ||
| 653 | |a Algorithms | ||
| 653 | |a Credit card processing | ||
| 653 | |a Decision trees | ||
| 653 | |a Financial institutions | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Song-Kyoo, Kim | |
| 773 | 0 | |t Mathematics |g vol. 13, no. 15 (2025), p. 2446-2461 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3239074511/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3239074511/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3239074511/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |