TY - GEN
T1 - Credit Card Fraud Detection using TabNet
AU - Meng, Chew Chee
AU - Lim, Kian Ming
AU - Lee, Chin Poo
AU - Lim, Jit Yan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The adopting of cashless payment methods, such as credit card payments and online transactions, has significantly enhanced the payment experience and added convenience to our daily lives. However, with the increase in cashless payment usage, financial fraud has become more sophisticated, posing a significant challenge to the security of these payment systems. In response, machine learning-based approaches have gained popularity in fraud detection. In this research paper, we propose the application of a deep tabular learning model, TabNet, for classifying transactions into fraudulent or non-fraudulent categories. TabNet utilizes a sequential attention mechanism to learn from tabular data. It comprises a series of decision steps where each step selects relevant features and updates the internal representation of the data. This mechanism enables the model to effectively capture complex, non-linear relationships between features, making it highly effective for fraud detection. The utilization of TabNet in fraud detection can contribute to strengthening the security of the payment system and reduce the chance of financial fraud. To evaluate the efficacy of our proposed approach, we conducted experiments on three widely used credit card fraud datasets, including the MLG-ULB dataset, the IEEE-CIS Fraud dataset, and the 10M dataset. Our experiments revealed that TabNet outperforms the state-of-the-art approaches across all three datasets, demonstrating its robustness and effectiveness in detecting fraudulent transactions.
AB - The adopting of cashless payment methods, such as credit card payments and online transactions, has significantly enhanced the payment experience and added convenience to our daily lives. However, with the increase in cashless payment usage, financial fraud has become more sophisticated, posing a significant challenge to the security of these payment systems. In response, machine learning-based approaches have gained popularity in fraud detection. In this research paper, we propose the application of a deep tabular learning model, TabNet, for classifying transactions into fraudulent or non-fraudulent categories. TabNet utilizes a sequential attention mechanism to learn from tabular data. It comprises a series of decision steps where each step selects relevant features and updates the internal representation of the data. This mechanism enables the model to effectively capture complex, non-linear relationships between features, making it highly effective for fraud detection. The utilization of TabNet in fraud detection can contribute to strengthening the security of the payment system and reduce the chance of financial fraud. To evaluate the efficacy of our proposed approach, we conducted experiments on three widely used credit card fraud datasets, including the MLG-ULB dataset, the IEEE-CIS Fraud dataset, and the 10M dataset. Our experiments revealed that TabNet outperforms the state-of-the-art approaches across all three datasets, demonstrating its robustness and effectiveness in detecting fraudulent transactions.
KW - Attention Mechanism
KW - Deep Tabular Learning
KW - Fraud Detection
KW - SMOTE
KW - TabNet
UR - http://www.scopus.com/inward/record.url?scp=85174408618&partnerID=8YFLogxK
U2 - 10.1109/ICoICT58202.2023.10262711
DO - 10.1109/ICoICT58202.2023.10262711
M3 - Conference Proceeding
AN - SCOPUS:85174408618
T3 - 2023 11th International Conference on Information and Communication Technology, ICoICT 2023
SP - 394
EP - 399
BT - 2023 11th International Conference on Information and Communication Technology, ICoICT 2023
T2 - 11th International Conference on Information and Communication Technology, ICoICT 2023
Y2 - 23 August 2023 through 24 August 2023
ER -