An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine
By A. Taha, Sharaf Jameel Malebary. IEEE Access, 2020
314 citations recorded by the source corpus.
New advances in electronic commerce systems and communication technologies have made the credit card the potentially most popular method of payment for both regular and online purchases; thus, there is significantly increased fraud associated with such transactions. Fraudulent credit card transactions cost firms and consumers large financial losses every year, and fraudsters continuously attempt to find new technologies and methods for committing fraudulent transactions. The detection of fraudulent transactions has become a significant factor affecting the greater utilization of electronic payment. Thus, there is a need for efficient and effective approaches for detecting fraud in credit card transactions. This paper proposes an intelligent approach for detecting fraud in credit card transactions using an optimized light gradient boosting machine (OLightGBM). In the proposed approach, a Bayesian-based hyperparameter optimization algorithm is intelligently integrated to tune the parameters of a light gradient boosting machine (LightGBM). To demonstrate the effectiveness of our proposed OLightGBM for detecting fraud in credit card transactions, experiments were performed using two real-world public credit card transaction data sets consisting of fraudulent transactions and legitimate ones. Based on a comparison with other approaches using the two data sets, the proposed approach outperformed the other approaches and achieved the highest performance in terms of accuracy (98.40%), Area under receiver operating characteristic curve (AUC) (92.88%), Precision (97.34%) and F1-score (56.95%).
Subfields: Accounting, Artificial Intelligence, Media Technology
View Paper: https://www.semanticscholar.org/paper/59b1eab7c0c6f89e1e028862a2ae26614ea69cd0
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Citations in the corpus
- A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects (2022, methodology): “Meanwhile, the LightGBM has been applied for different 547 classification problems, achieving excellent results [94], [95], 548 [96], [97], and its procedure is presented in Algorithm 3.”
- Credit Card Fraud Detection using Machine Learning Algorithms (2019, unknown): “(Taha and Malebary, 2020) have proposed a solution based on an Optimized Light Gradient Boosting Machine.”