Rule Learner can be applied across various business domains to automatically generate decision models with a minimal human involvement.
Banking: Banks use automatically generated and/or humanly adjusted decision models for fraud detection by flagging suspicious credit card transactions in real-time.
Insurance: Insurers can automatically discover business rules for dynamic pricing and assessing risk for claims and incorporated them in their complex decision models. Being used in the “ever-learning” mode, These rules may be regenerated to reflect market changes.
Healthcare: Rule Learner can assist doctors in diagnostics by analyzing medical images and patient data. They also help with monitoring and personalizing treatment plans.
Field Service: Rule Learner can be used to generate patterns such as service territory and skill levels based on historical information for previously executed services.
Customer Retention: Rule Learner may suggest best combinations of available services (such as calling plans, streaming, various subscriptions) based on the actual use by a customer over certain periods of time. The objective is to encourage customers to remain loyal and continue making purchases or using the provider’s services.