Credit Card Fraud Detection using Machine Learning Methods

As per the data transactions are made online, credit card fraud continues to be a growing problem. We present a new real-time, live detection approach for fraud in research. We cluster cardholders according to their transaction patterns and use a supervised machine learning approach to identify fraudulent behavior (card issuer wary of fraud). It is a massive problem in the financial industry and one of its biggest enemies, leading to major monetary losses with which millions or even billions can be achieved frontally. The model is iteratively updated by a feedback loop to adapt baffle responses against changing fraudster tactics. In this paper, we are using a logistic regression model in order to detect credit card frauds via machine learning. In this study, real-world credit card transactions with a great class imbalance (i.e. the number of frauds is far lower than that for normal activities) are used as a training data set. This is handled by steps like data preprocessing, normalization, encoding and sampling techniques intended to make the model robust: (i. e., more accurate). EDA — Since we can conduct our EDA on this dataset to see the overall pattern, which will be valuable in identifying what features are most important for fraudulent behavior. This data is then broken down into a training dataset and testing datasets to assess the predictions made by our model.