Tuesday, 27 March 2018

Impact Forecasting launches hail model for South Africa

Written by

Hail is a frequent natural peril in South Africa and one of the most damaging – causing insured losses of R1020million in October 2012 and R1400million in November 2013.

Compared to other regions, catastrophe reinsurance retentions for insurers are low in South Africa to reduce volatility. However, hail has been the main driver of catastrophe claims over the last five years which has led to great pressure on local insurers to increase deductibles, a sharp increase in catastrophe pricing and a substantial adjustment in catastrophe aggregate programmes.

This has created a need to improve insurers’ understanding of the frequency and severity of hail events and, most importantly, how the peril affects insurers and policyholders. In response, Impact Forecasting collaborated with Aon South Africa to create a hail model for the country that will help re/insurers better quantify and manage hail risk for both property and motor cover.

Pieter Visser, a Catastrophe Analyst at Aon South Africa says, “Local insurers are now retaining more hail losses than ever before so the new model is, therefore, a critical tool to quantify this risk. With increasing competition and more punitive catastrophe pricing, any information to optimise reinsurance in the working layers can save clients several million in cost. The hail model directly addresses this need.”

Enabling a competitive advantage for clients

The model will also enable insurers to find a competitive advantage in a highly competitive personal lines market. By helping to identify the concentration of exposures and risk composition of their books, clients will be able to determine how targeting a specific market can add or reduce volatility as part of their growth strategies.

All this is possible through the data and analytics from Impact Forecasting. Model developer Lukas Braun explains, “Significant hail events over the last five years have put this peril back on the radars of insurers but they were lacking information on severity and frequency. Now we have created an event set of 10,000 years with hazard based on 30 years of data from South Africa. This gives confidence to insurers to buy the most appropriate reinsurance and underwrite while managing the hail loss potential.”

Published in Energy and Environment