© 2022 IEEE.This work proposes using a fuzzy clustering approach to design territories for auto insurance rate-making and regulation in rate filing review. Our approach moves the focus from the current hard clustering method to a soft approach to improve the evaluation of territory risk for the rate-making purpose. Furthermore, we use non-negative sparse principal component analysis to smooth the estimate of risk relativities of basic rating units, which are Forward Sortation Areas (FSA) used in Canada. This additional step helps achieve the double sparsity of the fuzzy membership matrix by removing the small membership values and minor principal components to achieve a smoothing effect on the risk relativity estimate of FSA. Combining fuzzy clustering with non-negative sparse principal component analysis is particularly novel as it enables better decision-making of auto insurance rate regulation, where high-level statistics and major data patterns are desired.