Fraud Detection and Prevention using Artificial Intelligence and Machine Learning

Abstract

The rise of digitalization and the growing dependence on online transactions have made fraud a major issue for organizations, governments, and people at large. Ineffective at spotting new fraud trends, traditional fraud detection techniques are frequently reactive, time-consuming, and inefficient. Therefore, more proactive, effective, and accurate fraud detection and prevention methods are required to curb this issue or bring it to an acceptable level.

The purpose of this thesis is to apply Artificial Intelligence (AI) and Machine Learning (AI) in the detection and prevention of fraud. Due to the rise in fraud incidents across various sectors, the use of AI and ML methods in fraud detection has attracted a lot of attention recently. The main goal of this study is to establish how AI and ML can be used in fraud detection and prevention. The study suggests a comprehensive framework that includes various AI and ML techniques, including anomaly detection, supervised and unsupervised learning, and natural language processing. In order to identify and stop fraudulent activities, the suggested framework combines these techniques while taking into consideration the characteristics of fraud.

The study's findings demonstrate that the suggested framework has a high degree of precision in its ability to accurately detect and prevent fraudulent activities. The system is more capable of adapting to new data and learning from it thanks to the use of AI and ML methods, which also increases the system's ability to recognize new and evolving fraud patterns. This study offers insightful information about the application of AI and ML to fraud detection and prevention, and it helps organizations create efficient and effective fraud detection systems by shedding more light on the theme.