Abstract: The present application discloses systems and methods for systematically identifying situations, improving processes, and producing proactive, actionable results to address customer inquiries. The systems and methods produce actionable proactive results (i.e., actions to be taken by predicting a likelihood of a customer interacting with an organization, such as an insurance provider). This allows an organization to avoid unnecessary and/or repeat customer contacts and inquiries, which generate protracted work and the need for case management and escalations. In addition, the systems and methods improve first contact resolution metrics and customer experience, by predicting the need for an interaction without having to communicate with the customer.

Abstract: The present application discloses systems and methods for systematically identifying situations, improving processes, and producing proactive, actionable results to address customer inquiries. The systems and methods produce actionable proactive results (i.e., actions to be taken by predicting a likelihood of a customer interacting with an organization, such as an insurance provider). This allows an organization to avoid unnecessary and/or repeat customer contacts and inquiries, which generate protracted work and the need for case management and escalations. In addition, the systems and methods improve first contact resolution metrics and customer experience, by predicting the need for an interaction without having to communicate with the customer.

Abstract: According to some embodiments, an indication of a selected metric to be predicted, such as an insurance related metric, may be received. A system may analyze historical information to automatically determine a set of relevant underlying factors for that selected metric and a predictive model may be created. One or more future scenarios associated with an impact on relevant underlying factors may be defined, and a series of predicted impacted future values may be identified for those impacted relevant underlying factors. A series of impacted future values may then be predicted for the selected metric based on the predicted impacted future values and the predictive model.