pattern of fraud. These monitors were then fed into a neural network, which Data Mining In Telecommunications determined when there is sufficiently evidence of fraud to raise an alert. Data mining can also help detect fraud by identifying and storing those phone numbers called when a phone is known to be used fraudulently. If many calls originate from another phone to numbers on this list of "suspect" phone numbers, one may infer that the account is being use fraudulently (Cortes & Pregibon, 2001). Fraud applications have some characteristics that require modifications to standard data mining techniques. For example, the performance of a fraud detection system should be computed at the customer level, not at the individual call level. So, if a customer account generates 20 fraud alerts, this should count, when computing the accuracy of this system, as only one alert; otherwise the system may appear to perform better than it actually does (Rosset, Murad, Neumann, Idan & Pinkas, 1999). More sophisticated cost- based metrics can also be used to evaluate the system. This is important because misclassification costs for fraud are generally unequal and often highly skewed (Ezawa & Norton, 1995). For this reason, when building a classifier to identify fraud, one should ideally know the relative cost of letting a fraudulent call go through versus the cost of blocking a call from a legitimate customer. Another issue is that since fraud is relatively rare-and the number of verified fraudulent calls is relatively low-the fraud application involves predicting a relatively rare event where the underlying class distribution is highly skewed. Data mining algorithms often have great difficulty dealing with highly skewed class distributions and predicting rare events. For example, if fraud makes up only .2% of all calls, many data mining systems will not generate any rules for finding fraud, since a default rule, which never predicts fraud, would be 98.8% accurate. To deal with this issue, the training data is often selected to i