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Credit card fraud occurs often and results in significant financial losses [1]. The number of online transactions has increased dramatically, and online shopping has become increasingly popular. Credit card transactions account for a significant portion of these transactions. As a result, banks and financial institutions provide services. credit card fraud detection software has a lot of utility. demand. Fraudulent transactions can take many forms. and can be classified into several types. The subject of this paper is four main fraud occasions in real-world transactions. Each a series of machine learning models are used to combat fraud. An evaluation is used to choose the optimal method. This assessment gives you a step-by-step approach to picking the right company. With regard to the type of frauds and the appropriate method with a suitable performance, we demonstrate the evaluation. Real-time credit card fraud detection is another important aspect of our project. As a result, we Consider the implementation of predictive analytics to determine if a machine learning model and an API module are appropriate. Is a specific transaction authentic or fraudulent? We also evaluate an innovative technique for dealing with the skewed data distribution. The information we used in our research came from a variety of sources according to an open source and community- maintained website. Keywords: Credit Card, Fraud Detection Software, Open Source, Realtime credit card fraud, Skewed Data Distribution |
