Skip to main content

Improving Classification Performance in Credit Card Fraud Detection by Using New Data Augmentation.

Strelcenia, E. and Prakoonwit, S., 2023. Improving Classification Performance in Credit Card Fraud Detection by Using New Data Augmentation. AI, 2023 (4), 172-198.

Full text available as:

ai-04-00008-with-cover.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.3390/ai4010008


In many industrialized and developing nations, credit cards are one of the most widely used methods of payment for online transactions. Credit card invention has streamlined, facilitated, and enhanced internet transactions. It has, however, also given criminals more opportunities to commit fraud, which has raised the rate of fraud. Credit card fraud has a concerning global impact; many businesses and ordinary users have lost millions of US dollars as a result. Since there is a large number of transactions, many businesses and organizations rely heavily on applying machine learning techniques to automatically classify or identify fraudulent transactions. As the performance of machine learning techniques greatly depends on the quality of the training data, the imbalance in the data is not a trivial issue. In general, only a small percentage of fraudulent transactions are presented in the data. This greatly affects the performance of machine learning classifiers. In order to deal with the rarity of fraudulent occurrences, this paper investigates a variety of data augmentation techniques to address the imbalanced data problem and introduces a new data augmentation model, K‑CGAN, for credit card fraud detection. A number of the main classification techniques are then used to evaluate the performance of the augmentation techniques. These results show that B‑SMOTE, K‑CGAN, and SMOTE have the highest Precision and Recall compared with other augmentation methods. Among those, K‑CGAN has the highest F1 Score and Accuracy.

Item Type:Article
Uncontrolled Keywords:GANs; SMOTE; B‑SMOTE; data augmentation; imbalanced data; credit cards; fraud detection; fraud transactions; K‑CGAN
Group:Faculty of Science & Technology
ID Code:38336
Deposited By: Symplectic RT2
Deposited On:07 Mar 2023 16:55
Last Modified:07 Mar 2023 16:55


Downloads per month over past year

More statistics for this item...
Repository Staff Only -