Automatic Credit Fraud Detection Using Ensemble Model

Srinidhi, S.; Sowmya, K.S.; Karthika, S.

doi:10.1007/978-981-16-5655-2_20

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process will help in boosting the performance of the last classifiers. Many variants of boosting models have been developed including Adaboost, Gradient boosting, and XGBoost among others [6]. Stacking the third type of ensemble method (known also as stacked generalization) combines classifiers to find an improved model [7].…”

Section: Introductionmentioning

confidence: 99%

Identifying Fraudulent Credit Card Transactions Using Ensemble Learning

Jemai,

Zarrad,

Daud

2024

IEEE Access

View full text Add to dashboard Cite

Recognizing fraudulent credit card transactions is one of the main issues facing banking institutions. Since each transaction that completes the authentication procedure must be authorized by financial institutions, a hacker might pose as the actual cardholder and execute a fraudulent transaction. In this paper, we investigated the capacity of ensemble learning methods to identify credit card frauds on two distinct data sets: the Sparkov synthetic data set and the real data set of consumers in the European Union. XGBoost models, random forests, and naive Bayes classifiers are applied and assessed on both data sets. Accuracy, precision, recall, and F1 score are used to measure performance. According to the results, most ensemble classifiers perform exceptionally well on the real-world dataset, but significantly poorly on the simulated dataset. This study showed that, unlike in simulated environments, credit card transaction management scripts are quickly learned in deterministic settings. It is discussed that a larger danger of card information leakage results from strict determinism and lack of randomness.

show abstract

Section: Introductionmentioning

confidence: 99%