Automating Fraud Detection in Financial Services: An AI-based Approach

Abstract

One of the most common reasons why financial fraud occurs is due to credit card fraud. Unfortunately, traditional methods of detecting this issue have not been able to effectively prevent it. This has prompted the need for more sophisticated and efficient fraud detection techniques. Artificial intelligence has emerged as a promising tool for this problem. The paper looks into the use of AI methods to detect credit card fraud in the financial services industry. We analyze the performance of different algorithms, such as the Random Forest, the Neural Network, and the Naive Bayes. We also perform various preprocessing steps in order to get the data ready for analysis. The paper presents an evaluation of the four AI techniques in terms of their precision, recall, accuracy, and F1 score. It indicates that the Neural Network is the best performer when it comes to detecting credit card fraud. The study emphasizes the importance of utilizing AI-based methods for detecting financial fraud. It also highlights the potential of the Neural Network algorithm. The findings of the study have important implications for the advancement of financial fraud detection systems, and it can serve as a guide for future research. The study's findings provide valuable insight into the use of AI in identifying credit card fraud in the financial services industry. It also shows the potential of this technology to help combat this issue.