A Machine Learning Approach to Credit Card Transaction Fraud Prediction
Պահպանված է:
| Հրատարակված է: | SHS Web of Conferences vol. 218 (2025) |
|---|---|
| Հիմնական հեղինակ: | |
| Հրապարակվել է: |
EDP Sciences
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| Խորագրեր: | |
| Առցանց հասանելիություն: | Citation/Abstract Full Text - PDF |
| Ցուցիչներ: |
Չկան պիտակներ, Եղեք առաջինը, ով նշում է այս գրառումը!
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| 001 | 3274910704 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2416-5182 | ||
| 022 | |a 2261-2424 | ||
| 024 | 7 | |a 10.1051/shsconf/202521802017 |2 doi | |
| 035 | |a 3274910704 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 268334 |2 nlm | ||
| 100 | 1 | |a Liu, Zixuan | |
| 245 | 1 | |a A Machine Learning Approach to Credit Card Transaction Fraud Prediction | |
| 260 | |b EDP Sciences |c 2025 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a Credit card fraud has a significant impact on the financial industry and is now a growing concern. Machine learning can minimize bias against legitimate transactions and enable accurate identification of fraud. This study explores machine learning techniques to address category imbalances in credit card fraud detection datasets to mitigate economic losses while improving model performance. The results show that logistic regression outperforms other classifiers, including support vector classifiers (SVC), K-nearest neighbor classifiers (KNN), and decision trees, achieving an optimal balance between fraud detection and minimizing false positives. By conducting data processing techniques such as feature scaling and dataset balancing, the model shows an effective identification of fraudulent transactions that rarely exist in a vast number of legitimate transactions. In addition, simple neural networks trained on oversampled data reveal higher recall rates but at the cost of higher false positives, highlighting the tradeoff between accuracy and fraud detection sensitivity. These findings underscore the importance of choosing models that can both effectively detect fraud and minimize disruption to legitimate transactions, which also provide valuable insights for financial institutions seeking to enhance their fraud detection systems. | |
| 653 | |a Fraud | ||
| 653 | |a Machine learning | ||
| 653 | |a Data processing | ||
| 653 | |a Economic models | ||
| 653 | |a Decision making | ||
| 653 | |a Financial institutions | ||
| 653 | |a Models | ||
| 653 | |a Credit card fraud | ||
| 653 | |a Fraud prevention | ||
| 653 | |a Neural networks | ||
| 653 | |a Credit | ||
| 653 | |a Disruption | ||
| 653 | |a Identification | ||
| 653 | |a Credit cards | ||
| 653 | |a Transactions | ||
| 773 | 0 | |t SHS Web of Conferences |g vol. 218 (2025) | |
| 786 | 0 | |d ProQuest |t Sociology Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3274910704/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3274910704/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |