Rule based fraud detection systems use correlation, statistics, and logical comparison of data to identify potential ‘acts of fraud’ based on insights gained from previous (known) fraud incidents. They generally use traditional methods of data analysis and require complex and time-consuming investigations that deal with different domains of knowledge like financial, economics, business practices and behavior. Fraud often consists of many instances or incidents involving repeated transgressions using the same method. Fraud instances can be similar in content and appearance, but usually are not identical. Rule based systems rely on identifying a known fraud pattern.