Detection of Credit Card Fraud Transactions Using Machine Learning Algorithms and Neural Networks: A Comparative Study

Use of online transactions in day to day life has been increasing since last decade due to advancements in technology and network connectivity. Due to ease, simplicity and user friendliness of the online transaction system, new users are constantly joining the vast population benefitting from such system. Credit card fraud resulting from misuse of the system is defined as theft or misuse of one's credit card information which is used for personal gains without the permission of the card holder. To detect such frauds, it is important to check the usage patterns of a user over the past transactions. Comparing the usage pattern and current transaction, we can classify it as either fraud or a legitimate transaction. In this paper, the techniques used are KNN, Naïve Bayes, Logistic Regression, Chebyshev Functional Link Artificial Neural Network (CFLANN), Multi-Layer Perceptron and Decision Trees which are evaluated on basis of their result evaluated in terms of various accuracy metrics.