Abstract: One or more machine learning models are trained using data from disparate data training sets. Of particular interest are training sets relating to dispute resolution, and more particularly industry data and carrier data training set relating to insurance claims. The various data sets are used to produce machine learning models of varying fidelity, in which in which the amounts of known feature data range from more complete to less complete. Viewed from another perspective, the inventive subject matter also includes a computer-based predictive modeling system, in which a processor executes a predictive model, comprising multiple nodes and edges, in which some of the nodes store data relating to fixed modeling parameters, some of the nodes store data relating to variable modeling parameters, and some of the nodes store predicted outcomes.

Abstract: A system and method provide rewards or loyalty incentives to card member customers. The system includes an enrolled card member customer database, an enrolled merchant database, a participating merchant offer database and a registered card processor. The enrolled card member customer database includes transaction accounts of card member customers enrolled in a loyalty incentive program. The enrolled merchant database includes a list of merchants participating in the loyalty incentive program. The participating merchant offer database includes loyalty incentive offers from participating merchants. The registered card processor receives a record for charge for a purchase made with an enrolled merchant by an enrolled card member customer and uses the record of charge to determine whether the purchase qualifies for a rebate credit in accordance with a discount offer from the enrolled merchant.