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While Big Data Is a Boon for the Insurance Industry, It Is Crucial for the Reinsurer

There’s a reason big data and technology are disrupting the insurance industry. Ultimately, knowledge – or data – is power. ATTOM datasets help power analysis and limit the liability for reinsurance companies. Here’s how.

How profitable a reinsurance company is depends on its ability to understand and manage their exposure to risk, but it also depends on how they manage their clients’ risk considering factors such as their location, the environment, the economy, and local markets.

The Value of Data

Insurance companies can quickly become overwhelmed by a sequence of events. This can be particularly true for an insurer that serves a specific geographic location. Two consecutive disastrous wildfire seasons in California have created an insurance crisis for thousands of Californians in fire-prone areas. Insurers are facing $24 billion in unexpected losses, and the fall-out has been rate hikes for some customers or even abandonment by insurers.

The reinsurance company spreads out the risk exposure by promising to cover losses above certain limits, capping the insurer’s potential losses.

For the reinsurer, understanding the likelihood of events so that they can determine their own risk exposure, as well as that of their clients, is crucial. A holistic perspective allows for better planning. With a more confident outlook, reinsurers can better estimate cashflow and thus maximize both their future income from premiums, their income from investments, and their capacity to provide services.

An Indispensable Tool for Risk Modeling

Undercapitalized insurance companies can quickly fail after a catastrophic event, but so can reinsurers. Reinsurers must guard against competitors who enter the reinsurance market, drive down premiums, and reduce their margins. Reinsurers need every tool at their disposal to be competitive.

Knowledge is central to managing risk and adapting to the market. While no one can see what the future holds, parsing past data maximizes the likelihood that a decision will be the right one. Leveraging analytics based on big data is vital for a reinsurer when creating catastrophe models and setting rates.

The datasets from ATTOM form the basis for risk models that can be as specific as assessing the risk involved in providing flood insurance to a specific house. Property tax, deed, mortgage, foreclosure, environmental risk, natural hazard, and neighborhood datasets by ATTOM are based on more than 155 million U.S. residential and commercial properties multi-sourced from more than 3,000 U.S. counties.

Here are some ways, ATTOM’s data sets can boost the bottom line for reinsurers.

  • ATTOM datasets are at a granular level. For example, property characteristics and natural hazard data can give the reinsurer a clear picture of the rate and likelihood of sinkholes in a given neighborhood.
  • A dataset on historical weather patterns can provide local and seasonal predictions. Reinsurers can determine where their services are most needed and base coverage premiums based on demand.
  • ATTOM data facilitate insightful reports with probability graphs and risk and return charts. These show the costs and benefits of a company’s various reinsurance options.
  • Knowledge from big data allows the reinsurer to provide bespoke services. These services address the specific needs of an insurer and the catastrophes that that specific insurer may face based on the location, the regulatory climate, or the economy.
  • Datasets on competitor coverage could reveal new markets for the reinsurer, particularly markets or areas where they might have a competitive advantage.
  • Datasets on competitor activities might open up potential partnerships where leaning on the expertise of another firm could justify a higher rating and premium.
  • The datasets contribute to more accurate solvency stress tests so that reinsurers can be confident in their planning decisions.

In the future, all insurance companies will develop their own in-house reinsurance capabilities using similar datasets. For any business, controlling margins is what leads to profits, and leveraging big data is now a requirement for relevancy not just in the insurance market, but for real estate, mortgage lending, and marketing.

The above post is a theoretical use case.

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