# A Novel Ensemble Belief Rule-Based Model for Online Payment Fraud Detection
###### Abstract  
In recent years, with the rapid development of technology and the economy, online transaction fraud has become more and more frequent. In the face of massive records of online transaction data, manual detection methods are long outdated, and machine learning methods have become mainstream. However, although traditional machine learning methods perform well in fraud detection tasks, the lack of interpretability and class imbalance issues have always been pain points that are difficult to resolve for such methods. Unlike traditional methods, the belief rule base, as a rule-based expert system model, can integrate expert knowledge and has excellent interpretability. In this paper, we propose an innovative ensemble BRB (belief rule base) model to solve the credit card fraud detection problem by combining an ensemble learning framework with the BRB model. Compared with traditional machine learning methods, the proposed model has the advantage of high interpretability. And compared with traditional BRB models, the ensemble framework enables better performance in dealing with highly imbalanced classification tasks. In an experimental study, two datasets of credit card fraud detection from Kaggle are used to validate the effectiveness of this work. The results show that this new method can achieve excellent performance in the application of fraud detection and is capable of effectively mitigating the impact of an imbalanced dataset. belief rule base; imbalanced classification; fraud detection; ensemble learning 2020 ac academic Editor: [PERSON]  
9 December 2024 14 January 2025 1 February 2025 1 February 2025 4 February 2  
their reputation, lead to customer loss and may result in penalties from financial regulatory authorities in severe cases. The societal impact is also substantial, as credit card fraud is often associated with other criminal activities such as identity theft, money laundering and terrorism financing. Failure to effectively prevent such incidents can enable criminal activities and introduce instability into the financial system. Considering credit card fraud detection is related to information security, it is important to introduce the CIA triad (confidentiality, integrity and availability) [2]. Confidentiality refers to ensuring that information can only be accessed and used by authorized users. In credit card fraud detection, it is necessary to ensure that users' personal information is not accessed or disclosed by unauthorized personnel. Integrity refers to ensuring the accuracy and completeness of information, preventing unauthorized modification or destruction. In credit card fraud detection, this is reflected in the model's requirement to ensure the accuracy of the transaction data. If the transaction data are tampered with or lost, fraud detection judgments may be incorrect, leading to erroneous conclusions. Availability refers to ensuring that authorized users can access and use necessary information resources in a timely manner. This is achieved through measures such as backup, disaster recovery and load balancing to ensure continuous system availability. In the realm of credit card fraud detection [3; 4] applications, there is a pressing issue that cannot be overlooked--the problem of class imbalance. Class imbalance is a challenging issue in machine learning, primarily caused by a significant disparity in the number of samples between different classes. Most machine learning algorithms generally assume a balanced dataset to make more accurate predictions. However, in practical applications, many datasets exhibit class imbalance, with some classes having very few samples, such as in scenarios involving credit card fraud transactions [5], rare diseases [6], natural disasters [7] and software defect detection [8], where anomaly detection is crucial. This situation can lead to poor performance in the minor class, resulting in significant losses. In fraud detection, the limited yet crucial information within fraud samples often gets overlooked by traditional machine learning algorithms.