Despite wide acceptance of the IWA water balance as the basis of managing water losses, experience suggests that there are difficulties with its application. For apparent losses assessment, the traditional approach of deriving consumption profiles and testing water meters exceeds the resources of many utilities. While a few studies have explored alternative methodologies, these have largely not been validated and are susceptible to reproducibility and interpretation difficulties. This paper introduces an improved comparative billing analysis method that combines data preparation techniques, clustering analysis and classical regression analysis on monthly billing data of a water utility in Johannesburg, South Africa. Using the method, an average estimate of apparent losses due to metering errors of 8.2% was found against the best-case scenario of 9.4% using field investigations and laboratory tests, which also measure meter under-registration that the proposed methodology does not cater for. The validated results were possible at a fraction of the cost and effort, while also providing better insight into the underlying consumption patterns. The results show that data-driven discovery processes are viable alternatives for improved assessment and management of water losses.