Credit Card Fraud Detection using Classification, Unsupervised, Neural Networks Models

Abstract

Nowadays online transactions have grown in large quantities. Among them, online credit card transactions hold a huge share. Therefore, there is much need for credit card fraud detection applications in bans and financial business. Credit card fraud purposes may be to obtain goods without paying or to obtain unauthorized funds from an account. With the demand for money credit card fraud events became common. This results in a huge loss in finances to the cardholder. Previously they used the most common methods like rule-induction techniques, fuzzy system, decision trees, Support Vector Machines (SVM), K-Nearest Neighbor algorithms to detect the fraud transaction using a credit card. From our perspective, neural networks will generate more accurate results. To increase the accuracy and precision we use the algorithms Logistic Regression, K-Means, Convolution Neural Networks. Logistic Regression is a statistical model that tries to minimize the cost of how wrong a prediction is. CNN algorithm is used, to capture the intrinsic patterns of fraud behaviors learned from labeled data. So will make use of accuracy and precision to evaluate the performance of the proposed system.