The Imbalanced Classification of Fraudulent Bank Transactions Using Machine Learning
This article studies the development of a reliable AI model to detect fraudulent bank transactions, including money laundering, and illegal activities with goods and services. The proposed machine learning model uses the CreditCardFraud dataset and utilizes multiple algorithms with different paramet...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/11/13/2862 |
| _version_ | 1827734868045332480 |
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| author | Alexey Ruchay Elena Feldman Dmitriy Cherbadzhi Alexander Sokolov |
| author_facet | Alexey Ruchay Elena Feldman Dmitriy Cherbadzhi Alexander Sokolov |
| author_sort | Alexey Ruchay |
| collection | DOAJ |
| description | This article studies the development of a reliable AI model to detect fraudulent bank transactions, including money laundering, and illegal activities with goods and services. The proposed machine learning model uses the CreditCardFraud dataset and utilizes multiple algorithms with different parameters. The results are evaluated using Accuracy, Precision, Recall, F1 score, and IBA. We have increased the reliability of the imbalanced classification of fraudulent credit card transactions in comparison to the best known results by using the Tomek links resampling algorithm of the imbalanced CreditCardFraud dataset. The reliability of the results, using the proposed model based on the TPOT and RandomForest algorithms, has been confirmed by using 10-fold cross-validation. It is shown that on the dataset the accuracy of the proposed model detecting fraudulent bank transactions reaches 99.99%. |
| first_indexed | 2024-03-11T01:35:26Z |
| format | Article |
| id | doaj.art-a8392282f0b14419adff6e18f48cfe4a |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T01:35:26Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-a8392282f0b14419adff6e18f48cfe4a2023-11-18T17:02:25ZengMDPI AGMathematics2227-73902023-06-011113286210.3390/math11132862The Imbalanced Classification of Fraudulent Bank Transactions Using Machine LearningAlexey Ruchay0Elena Feldman1Dmitriy Cherbadzhi2Alexander Sokolov3Department of Information Security, South Ural State University (National Research University), Chelyabinsk 454080, RussiaDepartment of Mathematics, Chelyabinsk State University, Chelyabinsk 454001, RussiaDepartment of Mathematics, Chelyabinsk State University, Chelyabinsk 454001, RussiaDepartment of Information Security, South Ural State University (National Research University), Chelyabinsk 454080, RussiaThis article studies the development of a reliable AI model to detect fraudulent bank transactions, including money laundering, and illegal activities with goods and services. The proposed machine learning model uses the CreditCardFraud dataset and utilizes multiple algorithms with different parameters. The results are evaluated using Accuracy, Precision, Recall, F1 score, and IBA. We have increased the reliability of the imbalanced classification of fraudulent credit card transactions in comparison to the best known results by using the Tomek links resampling algorithm of the imbalanced CreditCardFraud dataset. The reliability of the results, using the proposed model based on the TPOT and RandomForest algorithms, has been confirmed by using 10-fold cross-validation. It is shown that on the dataset the accuracy of the proposed model detecting fraudulent bank transactions reaches 99.99%.https://www.mdpi.com/2227-7390/11/13/2862bank transactionsimbalanced classificationdetection of fraudulent transactionsmachine learning |
| spellingShingle | Alexey Ruchay Elena Feldman Dmitriy Cherbadzhi Alexander Sokolov The Imbalanced Classification of Fraudulent Bank Transactions Using Machine Learning Mathematics bank transactions imbalanced classification detection of fraudulent transactions machine learning |
| title | The Imbalanced Classification of Fraudulent Bank Transactions Using Machine Learning |
| title_full | The Imbalanced Classification of Fraudulent Bank Transactions Using Machine Learning |
| title_fullStr | The Imbalanced Classification of Fraudulent Bank Transactions Using Machine Learning |
| title_full_unstemmed | The Imbalanced Classification of Fraudulent Bank Transactions Using Machine Learning |
| title_short | The Imbalanced Classification of Fraudulent Bank Transactions Using Machine Learning |
| title_sort | imbalanced classification of fraudulent bank transactions using machine learning |
| topic | bank transactions imbalanced classification detection of fraudulent transactions machine learning |
| url | https://www.mdpi.com/2227-7390/11/13/2862 |
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