Citation: Dada et al (2019). Credit Card Fraud Detection using k-star Machine Learning Algorithm, 3rd Biennial Conference on Transition From Observation To Knowledge To Intelligence (TOKI 2019), held from August 15 to 16 in University of Lagos, Nigeria. Article In Press. 2 Credit Card Fraud Detection using k-star Machine Learning Algorithm DADA Emmanuel Gbenga Department of Computer Engineering University of Maiduguri, Maiduguri, Nigeria MAPAYI Temitope, OLAIFA Olowasogo Moses, OWOLAWI Pius Adewale Department of Computer Systems Engineering, Faculty of ICT, Tshwane University of Technology, Pretoria, South Africa Abstract. As the number of users opting for credit card payment is increasing daily worldwide, the threats posed by internet fraudsters on this type of payment are also on the increase. Banks, merchants and consumers globally have lost billions of dollars as a result of this type of fraud. The shortcomings of many of the existing credit card fraud detection techniques include their inability to effectively detect fraudulent transactions, the high false alarm rate, and high computational cost. These necessitated the development of more efficient credit card fraud prevention measures. Many models have been developed in the literature; however, the accuracy of the model is critical. In this paper, fraud detection model using K-Star machine learning algorithm is presented and the performance is evaluated using German Credit and Australian Credit datasets. The algorithm proposed in this paper proved to be highly effective and efficient with a resultant classification accuracy of 100%, very low false positive rate (0.00) and very high true positive rate of 1.00. All experiments are conducted on WEKA data mining and machine learning simulation environment. Keywords: k-star; classification; credit card; fraud detection; machine learning 1. Introduction Explosion in the growth of online shopping and e-businesses has mostly been ascribed to the increasing level of convictions and belief on information privacy provided by e-commerce sites (Andrea, 2015). Nevertheless, considering the evolving nature of fraud and theft perpetrated by fraudsters on e-commerce sites, one cannot but be deeply disturbed about the safety and privacy of credit card information (Akinyede, 2005). Credit card fraud is gradually becoming a very