WHAT WE DO
See the Resources page and the War Risk dashboard for an interactive example.
Civil war and organised violence continue to erupt throughout the world into the 21st century, with thousands of victims annually. In addition to the direct damage inflicted such conflicts also have a negative impact on the socio-economic environment, including but not limited to economic growth, willingness to do business and demographics. Armed conflict appears to be more likely in some countries than in others, and we use a data-driven approach to understand this likelihood and its main drivers. Retrospectively, for each conflict, the root cause or a complex of such causes can be assessed. It therefore can be useful to create early warning predictors for armed conflicts and potentially use them to understand what drives conflicts and wars. Such early warning indicators can play a key role in P&C ratemaking, especially in the Marine, Construction and Property LOBs.
Our model is designed not only to determine the degree of risk of conflict for each country in the near future but also to show the main objectively measurable indicators that can be linked to the emergence of conflicts that have already occurred and may affect the development of conflict in the future. The key engine of the Model is a Multi-layer Perceptron. This is a Supervised Feed-forward Neural Network with multiple layers which is trained to predict whether the country is in the conflict status or not, based on several input features sourced from reliable platforms such as the World Bank, International Labour Organisation, UN Population Program and the REIGN (Rulers, Elections and Irregular Governance) Databse among others.
The key production output from the Model is a world map where countries are ranked in accordance with the risk profile assessed by the model based on the likelihood of the conflict in each month in the next 3 years (up to the end of 2024):
The Model outputs for each country the assigned likelihood of being in each state (in conflict / not in conflict) which allows explicit ranking.The Model outputs for each country the assigned likelihood of being in each state (in conflict / not in conflict) which allows explicit ranking.
East Africa and Kenyajoseph.email@example.com
North Africa - Egyptahmed.firstname.lastname@example.org
North Africa - Francophonemohammed.email@example.com
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