Credit Card Fraud Detection System Using Clas...

Credit Card Fraud Detection System Using Classification Technique and Random Forest Algorithm

Author Name : D Vinay Yadav, Dr Vanitha Kakollu

ABSTRACT

In recent years, machine learning has been widely used for the fraud detection  process and achieved favourable performance. According to the financial sector’s have focused attention  recent computational methodologies to provide the credit card fraud problem. Our analysis provides a comprehensive guide to sensitivity analysis of current parameters with regards towords the current performance in credit card fraud detection. It defines only the numerical input variables which the help of the Principal Component Analysis (PCA) transformation. Unfortunately, due to confidentiality issues, we should not provide the original features and more background information to be provided. To predict machine learning model to predict whether a transaction is fraudulent or not by approaching logistics, support vector classifier, Random forest algorithms and identify the most important variables that may lead to higher accuracy in credit card fraudulent transaction detection. Additionally, we can compare and discuss the performance of various machine learning algorithms from the bank credit dataset with evaluation classification report from Principal Component Analysis and identify the confusion matrix and scalar metrics. So, present a framework of the parameter of the Machine learning topologies for the credit card fraud detection is to be enable financial institutions to reduce losses by preventing fraudulent activity towords the bank related process.

Keywords: Credit card fraud, Machine learning, Random forest algorithm, Confusion matrix, Scalar matrix, Support vector classifier