Securing Transactions: A Hybrid Dependable Ensemble Machine Learning Model using IHT-LR and Grid Search

TL;DR

This paper presents a hybrid ensemble machine learning model that combines multiple algorithms with grid search optimization and IHT-LR to improve credit card fraud detection accuracy and address data imbalance.

Contribution

It introduces a novel hybrid ensemble approach with IHT-LR and grid search, achieving state-of-the-art accuracy in credit card fraud detection on a large dataset.

Findings

Achieved up to 99.79% accuracy and 100% precision in fraud detection.

Outperformed existing methods and set a new benchmark.

Demonstrated effectiveness on a large, real-world dataset.

Abstract

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.