A design methodology for web-based services to detect fraudulent transactions in credit card

Financial fraud associated with the transactions of credit card leads to unauthorized access of performing credit card transactions in different platforms by intercepting important card credentials. In order to curb this problem, an effective fraud detection system is of primary importance for any financial institution. In the proposed methodology, a web-based fraud detection system has been designed considering two different protocols for the web-based services such as simple object access protocol (SOAP) and representational state transfer (REST). Further, for detecting the fraudulent transactions, these services are associated with five different machine learning techniques such as support vector machine (SVM), multilayer perceptron (MLP), random forest regression, autoencoder and isolation forest. The performance analysis of each machine learning algorithm associated with SOAP and REST services are critically assessed. The web services have been designed based on concepts of service oriented architecture (SOA) by considering a middleware family of software products i.e., Oracle SOA suite which is very often used by the software architects.