Credit Card Fraud Detection Using Supervised Learning Algorithms

Research Paper (postgraduate) from the year 2020 in the subject Computer Science - IT-Security, grade: 3.16, University of Engineering & Technology, Lahore (Lahore garrison university), course: ISD, language: English, abstract: Fraud is one of the most major ethical issues in Credit card industry. The main Purpose of our paper is to identify the Credit card fraud and provide a reasonable Solution to the fraud. Frauds caused by Credit Cards have costs consumers and banks billions of dollars globally. Even after numerous mechanisms to stop fraud, fraudsters are continuously trying to find new ways and tricks to commit fraud. Fraud detection is of immense importance in banking field and finance related companies. We are going to apply artificial neural network for detection purposes. Thus, in order to stop it we will provide a solution which will not only detect fraud but will detect it before it happening. Our system will learn from past committed fraud in order to detect new frauds. Mining algorithms had been applied to detect fraud but did not perform well. In our paper we are implementing machine learning algorithms to detect fraud in credit card transactions. The paper utilizes the supervised learning algorithms which are implemented on a dataset from kaggle which was highly skewed and imbalance. We balanced the set by robust scalar to have a 51 percent non fraud cases and 49 percent fraud cases. Logistic regression, random forest, decision tree and KNN has been implemented and further learning curves are displayed which shows which algorithm has the best ability to perform. The metrics used for evaluation are accuracy, specificity, precision and sensitivity and a comparative chart is established which displays the comparative analysis of these supervised learning algorithms.