Comparative Analysis of Back-propagation Neural Network and K-Means Clustering Algorithm in Fraud Detection in Online Credit Card Transactions

B.A. Abdulsalami; A. A. Kolawole; M.A. Ogunrinde; M. Lawal; R.A. Azeez; A.Z. Afolabi

doi:10.53704/fujnas.v8i1.315

Authors

B.A. Abdulsalami Department of Mathematical and Computer Sciences, Fountain University, Osogbo, Nigeria
A. A. Kolawole Department of Mathematical and Computer Sciences, Fountain University, Osogbo, Nigeria
M.A. Ogunrinde Department of Mathematical and Computer Sciences, Fountain University, Osogbo, Nigeria
M. Lawal Department of Mathematical and Computer Sciences, Fountain University, Osogbo, Nigeria
R.A. Azeez Department of Mathematical and Computer Sciences, Fountain University, Osogbo, Nigeria
A.Z. Afolabi Department of Mathematical and Computer Sciences, Fountain University, Osogbo, Nigeria

DOI:

https://doi.org/10.53704/fujnas.v8i1.315

Abstract

The ubiquitous nature of the internet had been a major driving force of the digital transformation in our world today. It has necessarily become the main medium for conducting electronic commerce (e-commerce) and online transactions. With this development, various means of possible payment methods have also emerged, such as electronic cash/ cheques, debit/credit cards, and electronic wallets. However, debit/credit cards are by far the most common payment methods employed. As a result, different credit card fraud activities have rapidly increased all over the world and are still evolving. This menace has drawn a lot of research interest and a number of techniques, with special emphasis on Data Mining, Expert System and Machine Learning (ML), as a means of identifying fraudulent behaviors. This paper examines and investigates two ML algorithms trained on public online credit card datasets, to analyze and identify fraudulent transactions. The BPNN and the K-means clustering ML algorithms were designed and implemented using Python Programming Languages. It was determined that the BPNN has a much higher accuracy of 93.1% as compared to the K-means which has an accuracy of 79.9%. Other metrics used to evaluate their performance also shows that the BPNN algorithm outperformed K-means algorithm, while the low prediction time of K-means gave it an advantage over the BPNN.

Keywords: Credit card, Fraud detection, Back-Propagation neural network, Clustering algorithm, Machine learning, Security.

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