DISTRIBUTED DATA MINING IN CREDIT CARD FRAUD DETECTION Project present a novel approach that strategically modifies a few transactions in the transaction database to decrease the supports or confidences of sensitive rules without producing the side effects. 

Since the correlation among rules can make it impossible to achieve this goal, in this paper we propose heuristic methods for increasing the number of hidden sensitive rules and reducing the number of modified entries.

 In past few years, the internet has become one the biggest fraud market mainly in there are many caused involved in banking systems. 

For past few years, banks used fraud warning systems for controlling fraud detection. In this paper, we propose a data mining techniques which can control these issues in commercial practice.

IN this process we analyze transaction information that efficiently computes fraud detections this is mainly done in e-commerce sites.

Fraud detection includes few technical problems which include skewed distribution of data training and nonuniform cost per error.

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