Fraud Detection Using Machine Learning and Deep Learning

Detecting fraudulent activities is a major worry for businesses and financial organizations because they can result in significant financial losses and reputational harm. Traditional fraud detection a method frequently depend on present rules and patterns that skilled scammer can easily circumvent. Machine learning and deep learning algorithms have surfaced as promising methods for detecting fraud in order to handle this problem. Authors present a thorough overview of the most recent ML and DL techniques for fraud identification in this article. These approaches are classified based on their fundamental tactics, which include supervised learning, unsupervised learning, and reinforcement learning. We review recent developments in each area, as well as their strengths and weaknesses. Additionally, we draw attention to some of the major problems with imbalanced datasets, adversarial assaults, and the interpretability of models as well as other important research tasks and difficulties in fraud detection. We also stress the value of feature science and data pre-processing techniques in enhancing the effectiveness of scam detection systems. Finally, we show a case study on the use of DL and ML techniques in the financial sector for fraud detection. Authors show how these algorithms can successfully identify fraudulent transactions, minimize false positives, and keep high precision and scalability. The overall aim of this article is to provide a comprehensive evaluation of the most cutting-edge ML and DL techniques for fraud identification and to shed light on potential future paths for this field of study.