In recent years, one of the most critical tasks for actuaries is to adopt data science techniques in predictive modeling practice. However, due to the peculiarity of insurance data as well as the priorities taken by actuaries in decision-making, such as the interpretability of models and regulatory requirements, most actuaries may find difficulties in applying them. We believe some original modeling methods with a good balance of high predictive accuracy and strong explanatory power is what is required. We propose, from this standpoint, AGLM (Accurate GLM), a simple modeling method with a desirable good balance accomplished by combining data science techniques and conventional Generalized Linear Models. For practitioners’ convenience, we have also developed an R package named aglm (https://github.com/kkondo1981/aglm). Since the first version released in January 2019, the aglm can make numeric features segmented optimally exactly as Fused LASSO does when the L1 regularization is designated. In addition, the current version can, alternatively if preferable, change them from linear variables to the optimal piecewise linear variables. Those functions make the constructed predictive model much more flexible than a conventional GLM hopefully still keeping sufficient explanatory power.