Business rules (BRs) have been widely adopted within decision making processes in the industrial fields (banking, assurance, transport ...). A BR is a high level description allowing non-computer scientists to author and/or make a decision by the use of vocabulary and concepts specific to the organization. It encompasses the business knowledge of experts and separates clearly the business logic from the application logic which implements it by defining and authoring it through a very structured and connected set of applications called a Business Rule Management System (BRMS). In this paper we propose to investigate the possibility of integration of probabilistic reasoning in a business rules-based system. As a consequence, we can deal with incoherent and incomplete data. Our approach is to extend an object BR model with a probabilistic model. This will be done by coupling business rules and probabilistic engines . The result will allow to perform inferences in Bayesian networks and Probabilistic Relation Models (PRMs) in order to sophisticate the calculations performed in classical BR inference.