Credit cards are now widely used by consumers for purchasing various goods and services due to widespread use of internet and consequential growth of E-commerce over the past few decades. This enhanced use of credit cards has increased the associated risks such as fraudulent use of credit cards that can cause financial loss to the card holders as well as to financial institutions. It is an ethical issue and has legal implications in various countries where laws and regulations forces financial intuitions and credit card companies to employ various techniques to detect and prevent the credit card frauds. Although the changes in technological systems also change the nature of frauds but data mining techniques such as classification, regression and clustering are very useful and are widely used to prevent and detect the frauds associated with credit cards. The credit card fraud prevention and detection functionality is a type of classification problem for the new customer as well for existing customers. There are multiple data mining techniques that can be employed for classification of customers and each has its own pros and cons. This study will compare four classification techniques namely Naïve Bayes, Bayesian network, Artificial Neural Network and Artificial Immune Systems for credit card transactions classification on a dataset obtained from a commercial bank in Pakistan. The major contribution of this study is use of real data on which extensive experiments have been performed and various results have been analysed with conclusion of best technique.