Telecommunication providers not only offer services but increasingly finance consumer devices. Credit scoring and the detection of fraud for new account applications gained importance as standard credit approval processes showed to fall short for new customers as there is only scarce information available in internal systems. Modern machine learning algorithms, however, can still infer intricate patterns from the data and thus can efficiently classify customers. Cost-sensitive methodologies can even enhance the savings. In this thesis, we develop a cost matrix which allows evaluating the individual risk of accepting a new customer and therefore helps to prevent new account subscription fraud optimally.