An Adaptive and Real-Time Fraud Detection Algorithm in Online Transactions

Abstract

While the Internet has made it possible to transact electronically and ubiquitously, some unscrupulous internet users have devised ways of defrauding e-commerce users. Several solutions have been designed and deployed to try and curb fraud in electronic transactions, but the news of fraud in e-commerce continues making the headlines globally. It is against this background that the researcher was motivated to design an adaptive algorithm that can detect credit card fraud as it occurs (real-time). The solution is based on the use of an Artificial Neural Network, Hidden Markov Model and a One-Time Password. The researcher used a synthesised dataset since a real dataset could not be found. The researcher tested the algorithm, which produced 100 per cent fraud detection rate and 98 per cent accuracy. The proposed solution can be made a plugin to e-commerce sites for the purposes of detecting and preventing fraud. The researcher was motivated to undertake this study after realising that while Zimbabwe is calling for the adoption of e-commerce due to the prevailing cash crisis, some people still have reservations due to security concerns. Despite, even in those countries where electronic commerce was adopted a long time ago, security is still a concern among e-commerce participants. The designed algorithm has a learning ability so that it can detect new fraud variations as they occur (real-time) and thus terminate the transaction should it be considered a fraudulent one. The author seeks to restore and instill confidence in people who transact online using credit cards.