Credit card fraud detection becomes critical due to increase in online transactions. Customers bought products online more often than not. The payment is either through debit or financials. The malicious users may attack the online information and hack credit and debit cards. Detection and prevention mechanisms thus are need of the hour. Researchers work towards achieving immunity against these attacks but perfection yet not achieved. This paper proposes similarity based decision tree approach for financial fraud detection strategy by working on state driven dataset. The objective is to detect the attack at early stage to avoid extravagant situations. The result is presented in the form of classification accuracy, precision and execution time. The result in terms of classification accuracy and execution time is improved by the factor of 10%.

Financial fraud, similarity based decision tree, classification accuracy, precision, execution time

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