This paper focuses on cluster modelling, which has proved to be a highly effective technique in achieving significant reductions in SST and Solvency II model run-times for life insurers, within a robust, practical and cost-effective framework. The applications of cluster modelling are broad, covering key areas from accelerated financial and capital reporting (including SST, Solvency II and MCEV), to ALM decision-making, to financial risk management (such as real-time monitoring of the ‘Greeks’), all within a consistent modelling framework. The technique is also complementary to other proxy-modelling techniques, such as Least Squares Monte-Carlo and replicating portfolios.