mprove results. For example, customer data is typically used to supplement call detail data when trying to identify phone fraud. DATA MINING APPLICATIONS The telecommunications industry was an early adopter of data mining technology and therefore many data mining applications exist. Several typical applications are described in this section. These applications are Chapter divided into three application areas: fraud detection, marketing/customer profiling and network fault isolation. Fraud Detection Fraud is a serious problem for telecommunication companies, leading to billions of dollars in lost revenue each year. Fraud can be divided into two categories: subscription fraud and superimposition fraud. Subscription fraud occurs when a customer opens an account with the intention of never paying for the account charges. Superimposition fraud involves a legitimate account with some legitimate activity, but also includes some "superimposed" illegitimate activity by a person other than the account holder. Superimposition fraud poses a bigger problem for the telecommunications industry and for this reason we focus on applications for identifying this type of fraud. These applications should ideally operate in real-time using the call detail records and, once fraud is detected or suspected, should trigger some action. This action may be to immediately block the call and/or deactivate the account, or may involve opening an investigation, which will result in a call to the customer to verify the legitimacy of the account activity. The most common method for identifying fraud is to build a profile of customer's calling behavior and compare recent activity against this behavior. Thus, this data mining application relies on deviation detection. The calling behavior is captured by summarizing the call detail records for customer, as described earlier in this chapter. If the call detail summaries are updated in real-time, fraud can be identified soon after it occurs. Because new behavior does not necessarily imply fraud, one fr