Exposure data standard needed to reduce accumulation & clash risk: RMS


Data standards for exposure information are required in the insurance and reinsurance industry to help the market better manage its accumulation and clash risk across lines of business, according to catastrophe risk modeller RMS and the University of Cambridge.

RMS worked with the Cambridge Centre for Risk Studies (CCRS) to develop a new data schema to help the industry better manage the more than $540 trillion of insurance exposure worldwide.

The pair developed a new insurance exposure data definition and framework which they believe will enable the industry to more efficiently manage multi-line accumulation and clash risk, across insurance and reinsurance contracts.

Such work is also very relevant to the insurance-linked securities (ILS) market, with ILS funds and investors now exposing themselves more to potential clash risks and accumulations through the expanding set of risk-linked assets the sector now invests in.

Today the pair announced the release of a new Data Definitions Document v1.0, which provides a schema for 14 different classes of insurance exposure including: casualty liability, specialty lines, trade credit and surety, agriculture, life and health, and annuity exposure, covering an estimated $554 trillion of total insured limits globally.

By developing the data schema RMS and the CCRS aimed to help identify potential concentrations of exposure, helping the industry to better assess accumulation risks.

The pair explain that insurance and reinsurance firms consider accumulation risk in a variety of ways. Such as through the underwriting of multiple insurance policies for the same policyholder; being exposed across multiple lines of insurance which have insured values located in the same area; and through ‘clash’ risk caused by underlying loss events that can hit firms across several classes of business within their portfolios.

The project to develop the schema looked at three potential loss scenarios with accumulation and clash risk attached.

These were:

  • A severe hurricane hitting the energy fields and marine installations in the Gulf of Mexico as well as personal and commercial lines properties;
  • An influenza pandemic that hits life and health insurers, as well as causing financial losses to the economy and stock markets;
  • And a geopolitical conflict located in Southeast Asia that triggers losses across all the major classes of insurance.

With the help of the research and schema insurance and reinsurance firms can better assess where they may be overexposed to such event scenarios.

This work is important to the sector, especially so in a time of emerging lines of business which have significant potential for clash and accumulations to occur, such as cyber risks.

With recent cyber losses, such as Petya / NotPetya, demonstrating the potential for cyber risk to affect property policies, re/insurers are going to need a better way to organise their underwriting data, to enable clash and accumulation risks to be analysed. This work will certainly assist here.

Dr. Mohsen Rahnama, Chief Risk Modeling Officer at RMS, commented on the launch, “We’re honored to partner with the Centre for Risk Studies on this data schema project. Establishing a current and market relevant data standard for managing exposure consistently is a priority for an industry managing accumulations and clash risk more widely. The release of this new data schema will solve risk challenges at an enterprise level across multiple classes of insurance. By making this schema open to the market, we hope to enable a new generation of risk model development and improvements across the insurance market in the ability to manage their multiline exposure risk.”

Professor Danny Ralph, Academic Director of Cambridge Centre for Risk Studies, added, “A standardized view of risk is necessary to enable a consistent understanding of exposure across multiple insurance portfolios. Understanding this, the Cambridge research team has engaged deeply with the insurance community to develop data standards that align with current practices and are practical to implement. We are pleased to partner on the development of this data schema with RMS and offer it as an open source document to the insurance and risk management industry.”

Getting the industry, both re/insurance and ILS, to standardise its exposure data will bring benefits, in helping companies to buy reinsurance and retrocession more effectively, manage their accumulation and clash exposure, as well as providing a standardised bedrock for risk transfer and trading.

All of this will benefit the industry, however the challenge will be in encouraging adoption.

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