Cat modeler launches new tool for Indonesia riot risk

Impact Forecasting, the catastrophe model development arm of Aon Benfield, has launched a new tool for determining the potential financial impact of riots, based on an analysis of such a threat in Indonesia.

The company says that understanding political dynamics and risk in Indonesia, the fourth largest population in the world, is increasingly important as it develops into “one of the world’s leading economic powers.”

The firm used data from the United Nations and the World Bank that covers more than 86% of all riots in Indonesia and documents the location, severity and catalyst for each event across the country, according to its release.

The new model includes:

• 600 unique events including the anti-Soeharto rioting in 1998, ethnic rioting in Kalimantan and sectarian violence between Christians and Muslims in Maluku
• Location of central hotspots and the catalyst for each riot
• Motives around elections, governance, popular justice, land, separatism and identity.

“The threat of rioting in Indonesia following the fall of the Suharto regime has had a significant impact on the property and life insurance markets in the country,” Malcolm Steingold, CEO of Aon Benfield for the Asia-Pacific region noted in a statement.

“Indeed, in the last 20 years rioting in Indonesia has led to over 6,900 fatalities and damage to over 40,000 properties highlighting the destructive properties of riotous behaviour. By understanding this risk, it will help insurers to grow their business in Indonesia while being fully aware of the risks and hotspots for violence,” he said.

“Riot modelling provides important information for insurers seeking to develop their capacity in emerging markets but are unsure of the stability of the country and, as a result, their insured assets,” added Mark Lynch, a terrorism, riot and political risk catastrophe model developer for Impact Forecasting.

“We have developed a map for each riot in Indonesia detailing the severity of potential property, life, and accident and health losses for each event. Modellers then overlay insurers’ exposures over each map to calculate the losses.”