Enhanced Financial Fraud Detection Using an Adaptive Voted Perceptron Model with Optimized Learning and Error Reduction
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| Yayımlandı: | Electronics vol. 14, no. 9 (2025), p. 1875 |
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| Yazar: | |
| Baskı/Yayın Bilgisi: |
MDPI AG
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| Konular: | |
| Online Erişim: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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|---|---|---|---|
| 001 | 3203193024 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14091875 |2 doi | |
| 035 | |a 3203193024 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Binsawad Muhammad | |
| 245 | 1 | |a Enhanced Financial Fraud Detection Using an Adaptive Voted Perceptron Model with Optimized Learning and Error Reduction | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Financial fraud detection is an important field in financial technology, and strong and effective machine learning (ML) models are needed to detect fraudulent transactions with high accuracy and reliability. Conventional fraud detection models, like probabilistic, instance-based, and tree-based models, tend to have high error rates, class imbalance problems, and poor adaptability to changing fraud patterns. These issues call for sophisticated methods that improve predictive accuracy while being computationally efficient. To overcome these limitations, this research introduces the Voted Perceptron (VP) model, which utilizes an iterative learning process to dynamically adapt decision boundaries based on misclassified examples. In contrast to traditional models with static decision rules, the VP model constantly updates its weight parameters, thus providing better fraud detection abilities. The evaluation compares VP with state-of-the-art machine learning models, such as Average One Dependency Estimator (A1DE), K-nearest Neighbor (KNN), Naïve Bayes (NB), Random Tree (RT), and Functional Tree (FT), by using important performance metrics, like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), True Positive Rate (TPR), recall, and accuracy. Experimental results show that VP outperforms its rivals significantly, yielding better fraud detection performance with low error rates and high recall. Furthermore, an ablation study confirms the influence of essential VP model elements on general classification performance. These results demonstrate VP to be an extremely effective model for detecting financial fraud, with enhanced flexibility towards evolving fraud patterns, and confirm the necessity for intelligent fraud detection mechanisms within financial organizations. | |
| 653 | |a Recall | ||
| 653 | |a Accuracy | ||
| 653 | |a Machine learning | ||
| 653 | |a Performance measurement | ||
| 653 | |a Adaptability | ||
| 653 | |a Root-mean-square errors | ||
| 653 | |a Credit card fraud | ||
| 653 | |a Fraud prevention | ||
| 653 | |a Decision making | ||
| 653 | |a Ablation | ||
| 653 | |a Support vector machines | ||
| 653 | |a Error reduction | ||
| 653 | |a Decision trees | ||
| 653 | |a Credit card processing | ||
| 653 | |a Distance learning | ||
| 653 | |a Error detection | ||
| 653 | |a Financial institutions | ||
| 653 | |a Business metrics | ||
| 773 | 0 | |t Electronics |g vol. 14, no. 9 (2025), p. 1875 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3203193024/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3203193024/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3203193024/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |