Enhancing B2B Fraud Detection Using Ensemble Learning and Anomaly Detection Algorithms

Abstract

This research paper explores the enhancement of business-to-business (B2B) fraud detection systems through the integration of ensemble learning techniques and anomaly detection algorithms. With the increasing sophistication and prevalence of fraudulent activities in B2B transactions, there is a pressing need for advanced analytical methods capable of identifying and mitigating such risks effectively. The study proposes a hybrid model that leverages the strengths of ensemble learning—combining multiple machine learning algorithms to improve predictive performance—and anomaly detection methods, which are adept at identifying unusual patterns indicative of fraudulent behavior. The research evaluates the effectiveness of this hybrid approach using a dataset containing diverse B2B transaction records, applying various ensemble techniques such as Random Forests, Gradient Boosting, and Voting Classifiers in conjunction with anomaly detection algorithms like Isolation Forest, Local Outlier Factor, and One-Class SVM. The results demonstrate a significant improvement in detection accuracy and reduction in false positives compared to traditional methods, underscoring the efficacy of the proposed model. This study contributes to the field by providing a robust framework for enhancing fraud detection in B2B environments, offering practical insights for businesses seeking to safeguard against financial losses and reputational damage from fraudulent activities. The paper concludes with a discussion on the implications of these findings for future research and real-world applications.