The researchers have already worked with many supervised and unsupervised methods for the purpose of credit card fraud detection. The supervised models have been found more efficient for the purpose of credit card fraud detection. The major goal of the credit card fraud detection research is to improve the accuracy while decreasing the elapsed time. The proposed credit card fraud detection models purposes the use of feature extraction and selection of the credit card data with linear regression algorithm for the credit card fraud detection. The feature engineering and analysis would be performed over the given trans-actional data and then final classification of the anomalies or outliers is done using linear regression classifier. The proposed model has been tested under the various experiments from the various groups of test cases. The test case groups have been ob-tained after applying the various levels of the feature elimination and feature selection over the collection of credit card transac-tion data. The proposed credit card fraud classification model is based upon two different models, which includes the Naïve Bayes and Support Vector Regression. The main aim of the research is to achieve the higher credit card fraud recognition accuracy, with the minimum classification complexity.

Credit card fraud detection, Early Fraud Detection, Regressive analysis, Linear regression models

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