Data modelling in the face of disaster

Experts discuss the importance of harnessing data and creating models to guide the catastrophe insurance sector

Data modelling in the face of disaster

Insurance News

By Gabriel Olano

The massive damage wrought by natural disasters in 2017 dealt huge losses to the insurance industry and underscored the need for insurers to increase understanding of these catastrophes to provide better protection while keeping the business afloat.

Insurance Business interviewed experts from global risk modelling and analytics company RMS about how improvements in data and modelling can help insurance achieve its goal of protecting people and their livelihoods from natural disasters.

Catastrophe models are objective, designed to quantify natural hazard risk as accurately as possible given current scientific knowledge and understanding of the performance of the built environment,” said Michael Drayton, a consultant for RMS. “Models are developed quite simply because the paucity of reliable historical loss data does not allow actuarial estimates of pricing to be made with confidence. While models output a technical premium, the market sets the price. Models follow physical rules and change only when new information comes to light. Markets also consider competitive pressures, regulation, economic cycles and customer behaviour, all of which can change unexpectedly.”

According to Drayton, markets follow a cycle, due to the rarity of massive catastrophes. He explained that each time a major loss doesn’t occur, confidence increases, reinsurance capital remains cheap and plentiful, and competition drives premiums down and coverages up.

“Markets typically harden rapidly after a large event and soften gradually as time passes,” he said.

Learning from catastrophes
Each catastrophe presents an opportunity to learn from it, and data gathered will be used to update risk models and create new ones, which incorporate more risks. Ryan Leddy, senior product manager at RMS, cited the example of the 2011 Tohoku earthquake and the subsequent tsunamis.

“The impact of the tsunami highlighted the importance of this sub-peril to the quantification of the financial risk posed by large offshore earthquakes, Leddy said. “First generation earthquake models built approximately 25 years ago could not explicitly model the risk from a tsunami. It would have required building an additional complex component to the model and the computational effort would have been just too big. Catastrophe models have evolved over time, have higher resolution, larger event sets, better hazard differentiation, more detailed vulnerability assessment, and include more sub-peril.”

RMS recently released a high-definition earthquake and tsunami model for Japan, which takes into account the connected risks these events present. This complements a Japan typhoon model it released in 2016, making Japan the first market where RMS has created an HD model for both earthquakes and typhoons.

“[The model] incorporates a probabilistic tsunami model fully integrated with the earthquake stochastic event set, allowing for assessment of the combined risk posed by earthquakes and their resulting tsunami,” Leddy said.

He explained that the new model provides a comprehensive view of earthquake risk in Japan by assessing the risks from five potential sources of loss: ground shaking, tsunami inundation, fire following earthquake, liquefaction, and landslides. 

“The included predictive modelling approaches for liquefaction and landslides allow better risk differentiation, underwriting and risk management decisions,” he said.

The Asia-Pacific situation
According to Hemant Nagpal, Asia’s markets are so diverse and encounter different challenges. On one hand, mature markets may be struggling for growth and diversification, while on the other, emerging markets such as China and India are spearheading growth in the region.

“The solvency status of the whole industry is strengthening in China with the merger of banking and insurance regulators earlier in 2018,” Nagpal said. “It is set to tighten oversight and curb financial risks, in the biggest revamp of the financial sector in China since 2003. China Re Cat Research Center, launched in June 2017, aims to develop a set of catastrophe models over time [that are] possible for in-house use and as nationwide standard models providing an in house view of cat risk and diversification to modelling.”

As for India, the nation of over one billion people has taken steps to spur the growth of insurance through both the public and private sectors.

“Changes in regulations to allow cross border reinsurers to set up branches in India may lead to increased competition in (re)insurance and a need for innovative product development,” Nagpal said. “The Indian government has begun to address the protection gap, introducing a US$3billion+ crop insurance scheme (PMFBY) to help farmers cope with crop failure due to natural disaster.”

Meanwhile, he said that Singapore is also making progress in fostering natural catastrophe research.

“We are also seeing new forms of insurance products and solutions, such as index-based and parametric insurance,” he said. “Big data analytics is at the core of Singapore’s own Natural Catastrophe Data Analytics Exchange (Nat Cat DAX). Laos, Myanmar, and Cambodia will establish a regional catastrophe risk insurance pool under SEADRIF in 2019, with the support of Japan, Singapore and the World Bank.”



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