Abstract: To predict client sensitivity to rate changes for commercial deposit products, a machine learning model is trained using (i) first attributes associated with a corresponding first client during a first rate change, (ii) an indication of whether the first rate change was requested by the corresponding first client or by a corresponding first bank, and (iii) an indication of whether the corresponding first client is sensitive to the first rate change. A second set of attributes is obtained which is associated with a second client having a second commercial deposit product. A machine learning engine applies the second set of attributes and an indication of an initiator of a potential second rate change to the machine learning model to predict client sensitivity to the potential second rate change. Then, a recommendation is provided as to whether to change the second rate based on the predicted client sensitivity.