Using bank customer churn data, we demonstrate the explanatory and predictive capacity of monotonic decision rules. Since the data are partially ordinal, they are structured by a new version of the Variable Consistency Dominance-based Rough Set Approach before the induction of monotonic decision rules. The induced rules characterize loyal customers and the ones who left the bank. Such an approach is in line with explainable AI, aiming to obtain a transparent and understandable decision model. In the course of a computational experiment, we compare the predictive performance of monotonic rules with several well-known machine learning models.