A new predictive strikes, riots, and civil commotion data model providing forecasts for thousands of global locations on the potential for damaging civil unrest events to emerge, has been launched by Verisk Maplecroft.
Developed for political violence underwriters, exposure analysts, modellers, and specialty reinsurers as part of the Lloyd’s Lab accelerator programme, the SRCC Predictive Model aims to offer insurers a new approach to how they assess and price these risks.
Sam Haynes, head of analytics at Verisk Maplecroft, said: “Insured losses linked to major bouts of unrest have reached new highs in recent years, while our data tells us that in the last 12 months, SRCC risks have risen in over 50% of countries. As these risks expand, so too does the need for granular, forward-looking data that provides valuable insight into exposures.”
The model provides 12-month forecasts for 50,000 counties and districts globally on the risk of severe protests occurring that could result in insured losses. The machine learning model validates its predictions against actual insured losses and draws on geospatial data covering the size of recent protests, concentrations of economic value, demographics, and a range of political risk, climate, and socio-economic indicators.