Since credit scoring was first applied in the 1940s the standard methodology has been to treat consumer lending decisions as a binary classification problem, where the goal has been to make the best possible ‘good/bad’ classification of accounts on the basis of their eventual delinquency status. However, the real goal of commercial lending organisations is to forecast continuous financial measures, such as contribution to profits, but there has been little research in this area. In this paper continuous models of customer worth are compared to binary models based on customer behaviour. Empirical results show that while models of customer worth do not perform well in terms of classifying accounts by their good/bad status, they significantly outperform standard classification methodologies when ranking accounts based on their financial worth to lenders.