  Financial institutions and businesses face an ongoing challenge from
fraudulent transactions, prompting the need for effective detection methods.
Detecting credit card fraud is crucial for identifying and preventing
unauthorized transactions.Timely detection of fraud enables investigators to
take swift actions to mitigate further losses. However, the investigation
process is often time-consuming, limiting the number of alerts that can be
thoroughly examined each day. Therefore, the primary objective of a fraud
detection model is to provide accurate alerts while minimizing false alarms and
missed fraud cases. In this paper, we introduce a state-of-the-art hybrid
ensemble (ENS) dependable Machine learning (ML) model that intelligently
combines multiple algorithms with proper weighted optimization using Grid
search, including Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor
(KNN), and Multilayer Perceptron (MLP), to enhance fraud identification. To
address the data imbalance issue, we employ the Instant Hardness Threshold
(IHT) technique in conjunction with Logistic Regression (LR), surpassing
conventional approaches. Our experiments are conducted on a publicly available
credit card dataset comprising 284,807 transactions. The proposed model
achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a
perfect 100% for the DT, RF, KNN, MLP and ENS models, respectively. The hybrid
ensemble model outperforms existing works, establishing a new benchmark for
detecting fraudulent transactions in high-frequency scenarios. The results
highlight the effectiveness and reliability of our approach, demonstrating
superior performance metrics and showcasing its exceptional potential for
real-world fraud detection applications.
