CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

The banking industry values cyber security due to an increasing risk of cyber attacks and crime. Credit card cyber fraud poses a significant security problem throughout the world. Conventional anomaly detection and rule-based algorithms are popular methods for identifying cyber fraud, but they are time-consuming, resource-intensive, and incorrect. Machine learning is one of the technologies acquiring. Popularity and key influence in this industry. This paper reviews and consists prior research on credit card cyber fraud detection. This review focuses on machine and deep learning methods. Our evaluation found 181 research publications published between 2019 and 2021. This article provides an overview of machine learning and deep learning methods and their use in detecting credit card fraud. When using machine learning to identify credit card fraud, it’s crucial to select appropriate features. Credit card fraud detection is a classic example of categorization. We investigated and pre-processed data sets before using anomaly detection techniques like Local Outlier Factor and Isolation Forest on PCA-transformed credit card transaction data.