Although Bayesian Networks (BNs) are increasingly being used to solve real world problems [47], their use is still constrained by the difficulty of constructing the node probability tables (NPTs). A key challenge is to construct relevant NPTs using the minimal amount of expert elicitation, recognising that it is rarely cost-effective to elicit complete sets of probability values. This thesis describes an approach to defining NPTs for a large class of commonly occurring nodes called ranked nodes. This approach is based on the doubly truncated Normal distribution with a central tendency that is invariably a type of a weighted function of the parent nodes. We demonstrate through two examples how to build large probability tables using the ranked nodes approach. Using this approach we are able to build the large probability tables needed to capture the complex models coming from assessing firm's risks in the safety or finance sector. The aim of the first example with the National Air-Traffic Services(NATS) is to show that using this approach we can model the impact of the organisational factors in avoiding mid-air aircraft collisions. The resulting model was validated by NATS and helped managers to assess the efficiency of the company handling risks and thus, control the likelihood of air-traffic incidents. In the second example, we use BN models to capture the operational risk (OpRisk) in financial institutions. The novelty of this approach is the use of causal reasoning as a means to reduce the uncertainty surrounding this type of risk. This model was validated against the Basel framework [160], which is the emerging international standard regulation governing how financial institutions assess OpRisks.