Abstract: A computer implemented method and a computer system implementing the method provide enterprises with pre-emptive/proactive operational risk management. First, historical data on the occurrence of operational risk events and other internal business/external metrics and indicators are collected. This is followed by construction of a model for correlating the risk events with internal and external metrics and indicators. This can result in the estimation of the probability of occurrence of risk events and a model for the severity of a loss event (in termns of, say, dollar amount) as a function of the various variables that are related to or have leverage on the business operation. The Key Risk Indicators for the business are then identified based on the model. Following this, the identified key risk factors are forecasted for future time periods and used to identify early warnings of risk and is further validated.

Abstract: The present invention has the aim of making more useful the representation of a Bayesian belief network by incorporating programmable logic that extends and improves the capabilities of the network as an engine for a decision-support system. The improved belief network enables a user to create and evaluate one or more conditional states by converting one or more network nodes to function as a logical gate or switch that can turn “on” a new network node.

Abstract: A method of dynamically creating a database is comprised of receiving event data from a plurality of independent agents input according to a common taxonomy that exposes the event in its molecular terms, e.g., causal factors driving the event and mitigating factors related to the event, and storing the event data. The molecular terms may be weighted. Additionally, the agents inputting the event data may be authenticated to insure that data is being entered by only those parties authorized to do so. The event data may also be validated by reference to external sources of information. The event data may additionally be normalized, anonymized and scaled. Synthetic event data may be added to the database for those situations where actual data is not available or is not very comprehensive. The synthetic event data may be generated by one of a test bed or a subject matter expert.