Following the global health crisis caused by the Covid-19 pandemic, and to save lives, Morocco has chosen isolation, containment and general closure in order to slow the spread of the virus. These drastic decisions put the national economy on a partial halt. The economic cost is difficult to assess but the repercussions can unfortunately be felt for many years.In this article, we will specify the impact of the Covid-19 crisis on the Moroccan economy, we will also project the main measures currently taken by the government to deal with the adverse effects of the Covid-19 health crisis. Then, we will present macroeconomic proposals that could serve to bring Morocco out of this economic depression and revive the economy in a short time.

By looking for a formal method for the management, the use of the Bayesian networks came a natural choice. The Bayesian networks offer a formal frame, but also the ease of learning, and the adaptation to the new data or the requests. In the literature there are numerous examples which present the use of the Bayesian networks in the risk management in diverse sectors such as: financial, medical or legal. In this article we chose the domain of the insurance. Indeed, we propose an example of application of the Bayesian networks at the risk of error in an insurance company.

This paper aims to present the main lines of the Extreme Value Theory applied to the operational risk. The idea is to present a methodology which allows to identify a threshold by type of risk, and to feign the losses below the threshold with the classical laws, and the losses above with a Generalized Pareto Distribution (GPD).
The adequacy of the data to the law GPD allows to consider an extreme quantile, as minimal strategy, sensitive to the size of samples, and to plan random costs whose probability of occurrence is very low, but the choice of the threshold beyond of which the observation will be judged extreme, is a point to be handled with precaution, even if we propose a technique to quantify this threshold.
Furthermore, the costs of extreme losses do not lend themselves to modeling ; by definition this type of costs is rare, and the forecasts or the estimations must be often established with a big distrust, and outside the available data. The models must be used in a supple way, without believing completely to the limit.
The adoption of this method could allow the risk managers to observe the extreme events with a certain objectivity, to check the hierarchical organization of the classes of operational risks, and in the other hand, establish reserves to face these risks.