Fraud Detection Using Artificial Intelligence

Abstract

With the rising prevalence of digital transactions and online services, the risk of fraudulent activities has grown exponentially. Traditional rule-based systems for fraud detection often struggle to keep up with evolving tactics used by malicious actors. This research delves into the application of machine learning modules in detecting fraud across various domains, such as financial transactions, insurance claims, and online platforms. Moreover, the study investigates the combination of these methods in ensemble approaches to achieve higher accuracy and robustness. Additionally, feature engineering and data pre-processing techniques play a crucial role in enhancing model performance. We delve into the identification and selection of relevant features and the impact of different data transformations on the detection process. Furthermore, addressing the challenge of imbalanced datasets, we examine techniques like oversampling, under sampling, and synthetic data generation to mitigate class imbalance and enhance fraud detection efficiency. To evaluate the models, we utilize publicly available benchmark datasets and conduct comparative analyses to highlight their strengths and weaknesses. The results showcase the superiority of certain models for specific fraud scenarios and underscore the importance of a tailored approach for different domains.