ABSTRACT
The challenges faced by telecommunication industries are many, there is need to eliminate or curb some of these challenges and that is when a decision support system comes into play. A decision support system (DSS) is one which can aid to strategize management within industries to make vital decisions in relation to customer and network data. This enables managers in their areas within their industries to fully utilize the amount of data available to make decisions in relation to utilization of valuable and important resources. This research project therefore, focused on designing a fraud detection system with minimum false positive alerts using decision tree learning. The system has been trained to learn from training data and used decision tree algorithms to make predictions on future telecommunication data. Fraud detection is hard, so it is not surprising that many fraud systems have serious limitations. Different systems may be needed for different kinds of fraud with each system having different procedures, different parameters to tune, different database interface, different case management tools and features.
