On this paper, fee profiles for contributors are recognized by making use of clustering strategies to TARGET2 knowledge. Fee profiles describe the overall fee habits of contributors and are, due to this fact, related for a number of dangers, reminiscent of liquidity, credit score and operational dangers, that fee techniques face. After presenting the challenges of making use of cluster evaluation strategies to funds knowledge, normal fee profiles are derived from the pooled outcomes of a number of runs of the k-means clustering algorithm with completely different similarity measures. Ten completely different fee profiles with completely different normal intraday fee behaviors had been decided on this approach. By figuring out the deviations of every participant from their profile each day, the steadiness of the fee profiles was checked for robustness.