Risk management

By definition, risk (technological, environmental, military, health or financial) is intrinsically linked to the notion of the unforeseeable. Particularly adapted to modelling knowledge and cognitive processes in areas containing uncertainties, Bayesian networks are today recognized as being the most appropriate mathematical framework for structuring and objectivising any thought on risk control and management in emergency situations.

8 reasons why a risk manager should use BayesiaLab

Relying on the conceptual wealth of this theoretical formalism, the technologies developed by Bayesia and used in the BayesiaLab software, provide practical and effective answers to your needs:

Through the modelling of expert knowledge by the Bayesian network you can formalize the full risk chain. The graphical representation of Bayesian networks and BayesiaLab's ergonomic design make it an invaluable brainstorming and communication tool.

If feedback data is available, you can use the power of unsupervised learning to discover the set of significant probabilistic relations present in your data and, therefore, identify the links between your risk factors. This kind of learning can be a real knowledge finding tool, helping one understand the phenomena.

Supervised learning will allow you to use your data by characterizing your main risk (e.g. claim, fraud), by identifying the minimal subset of really relevant risk factors.

BayesiaLab can rigorously take your expert knowledge and your feedback data into account (update of Bayesian expert knowledge according to feedback data).

You may test the lever effect (e.g. implementation of risk reduction actions) by enriching your probabilistic models with actions. The association of costs with these levers will allow you evaluate different policies.

These adaptive questionnaires will suggest the most relevant questions in terms of information contribution to your main target and in terms of costs associated with the questions (cost of acquiring the information). When you have made your choice, the answer will be taken into account to determine new more relevant questions.

Thanks to the BayesiaLab analysis toolbox, you can really understand your probabilistic models: analysis of the strength of relations, analysis of relations between target and other variables, analysis of relations between your main risk and other variables, analysis of observations in order to know whether all the observations are fully concordant or whether some are contradictory.

Finally, you can interact in a convivial way with your networks to test all your hypotheses.

Overview describing our specific technological offer in relation to risk control.

Discover BayesiaLab, our generic decision aid software package based on Bayesian networks »

Research projects

Bayesia also develops consortium research projects concerning risk control and management of emergency situations.

Examples of applications

• Salmonella isolation Identification of factors associated with Salmonella isolation on pork carcasses via bayesian networks.

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• Risk analysis and safety policy: example of transporting people Risk analysis for transporting people; modelling of a prevention policy.

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