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パラメトリック保険とIOTにおけるデータの重要性

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2021/08/10
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The world is witnessing a state of flux, with climate change threatening where we live, work, and play. Natural catastrophes and weather reliant industries such as agriculture are witnessing higher risks and impact. Technology advancement has brought about a double edge sword, whereby we enjoy the convenience it brings yet are also exposed to cyber risks and internet connectivity issues. The pandemic has also revealed itself to be a global challenge to overcome and has interrupted businesses greatly. Supply chain must be strengthened to prevent the chain from breaking, which posed risk for business operations. These challenges are serious concerns for corporations and insurers are expected to cover such risks. But with uncertainty surrounding recent events, insurers need to better understand, model, price, and react to heightened risk that threaten enterprise globally.

Parametric insurance is a term used to describe when insurance cover can be triggered or a claim settled automatically dependent on the fulfilment of data driven parameters and wider events, such as a natural weather catastrophe, a location dependent event or behavior. Use cases for parametric insurance are more commonly found in general and healthcare insurance today, and often linked to major natural catastrophes, automotive, logistics, medical devices and agriculture, such as the IoT use cases discussed in this report. In addition to claims settlement or coverage triggers, parametric solutions are also being deployed for pricing models, risk monitoring, preventative measures, and real-time reporting for events such as internet outage, flight delays, natural catastrophes such as floods, earthquake, and tropical cyclone.

Celent has kept track of IoT developments and its impact on the insurance industry as far back as 2014. In the diagram below, we summarize the interactive components of IoT.

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Figure 1: Interaction Among the Three Components of the Internet of Things

Source: Celent Report, The Internet of Things and Property/Casualty Insurance: Can an Old Industry Learn New Tricks?, April 2014

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Today, we are witnessing greater emphasis of sensor data capture and parametric models which can mitigate risk by helping clients better prepare for or avoid the event from occurring. Parametric coverage is focused on physical and financial risks, with more immediate and real-time feedback. Data analytics is key to the success of parametric assessment and IoT device provide the hardware to collect the required data.

Insurtechs with Parametric Solutions

The market for parametric insurance is growing – both in end-customer propositions and in solution provider technology. In this blog, we look at just two insurtechs which have recent operations in APAC and who are making strides in parametric solutions using data analytics and machine learning.

Descartes Underwriting handles massive new data sources (including IoT, satellite imagery, sensors, radar, sonar, and third-party data) and utilize proprietary data integration algorithms for machine learning risk models which provide underwriting insights. Descartes’ goal is to protect against natural catastrophes and climate risk events affecting agriculture, forestry, and energy across all trade sector, in over 60 countries around the world. Descartes data-driven parametric coverage keeps costs low while offering precise protection through the usage of recent data sources to complement claims histories data. The integrated data are fed into a machine learning pipeline which include image recognition techniques and statistical analysis methods to capture trends and climate change impact. The approach includes engaging with brokers to discuss perils and climate risk that clients are facing, which is followed by parameters development customized to the client such as GPS coordinates and preferred risk period. Once parameters are determined, models for parametric protection are developed and covers previously uninsurable climate risks. The policy parameters are then monitored in near real-time using data from government statistics, weather station data, on-site sensors, and so on. When events are triggered, clients are notified and swiftly indemnified.

Riskwolf provide parametric and connectivity insurance to protect against network connectivity issues typically caused by weather, cable cuts, and local incidents. Network connectivity is important due to the expansion of the digital economy and more work-from-home arrangements triggered by the pandemic. They shared with me that they utilize independent data providers such as regional internet availability index, internet traffic and incident reports, cloud service status, and the dark web to develop connectivity risks model. According to Riskwolf, the protection gap in telecommunication is estimated to be $4 billion in ASEAN and India alone, with a single telecommunication market in ASEAN estimated to be $30 million. In Southeast Asia, a global reinsurance company uses Riskwolf to provide ISPs and telecommunication companies the possibility of giving something back in case uncontrollable disruptions happen. As of writing, Riskwolf are currently onboarding two global leading reinsurers and plan to launch an ASEAN telecommunication product in the second half of 2021. The goals for 2021 is to launch the first productive Outage Benefit plan in Southeast Asia and Europe and expand the platform to include pricing for the Internet Index and Cloud Outage protection, with capital raising round for the company further growth and expansion.

Analysis and Opinion

In my opinion, Descartes and Riskwolf are entering at an important time for the Asian market, which is increasingly being affected by the detrimental effects of climate change. Asia as a region faced hazards including extreme precipitation, flooding, drought, severe typhoons, and rising heat and humidity. Drastic changes in weather will affect infrastructure such as network connectivity, which is becoming a must-have today. Therefore, traditional insurance risk assessment and products must change, and not assess risk based on historical loss records alone. The inclusion of parametric solutions and data analytics will improve corporations’ decision-making cycle and can mitigate or avoid loss resulting from climate change or business operation disruption. The above insurtechs signal a trend towards a greater use of data sources for risk model development and assessment, including IoT and sensor data. The reinsurance and insurance industry will significantly change the traditional insurance data metrics and provide avenues for new business models and partnerships. Partnerships between reinsurers/insurers and IoT solution providers are already beginning, with Munich Re (through its subsidiary Hartford Steam Boiler) acquiring relayr, a provider of industrial and commercial IoT solution, to develop data solutions for risk management and financial instruments. And recently, Chubb acquired StreamLabs, an IoT maker of smart water monitoring and leak detection products, to advance Chubb’s strategy of offering best-in-class “predict and prevent” risk engineering services for consumer and commercial clients.

As can be seen from the select examples above, parametric insurance is a growing area of interest and is an area to watch in the coming years. The benefits of parametric assessment are increased data usage and expanded predictive analytics capabilities for the reinsurer/insurer, which translates to straight-forward and affordable coverage for their clients. Compared to traditional insurance, there will be a reduction of uncertainty around risk predictors. High uncertainty leads to higher premiums, or even no coverage, for customers. Traditional insurance has higher costs as well, due to claims assessors and other requirements for claims.

However, the challenge of IoT and parametric insurance is identifying the right data source and will benefit from a consultative approach with clients to customize the strategy and product offerings, such as an investment of physical sensors for IoT monitoring. There are also considerations for IoT data stream disruption and contingency for missing data. Solutions must be developed to cater for data fallback in platforms and on IoT data integration with the wider cloud infrastructure.

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To learn more, Celent tracks this market and has research addressing it (list of recent reports here).If you would like to find out more, please feel free to get in touch with me.

Below are related reports/blogs to this blog:

Internet of Things, Augmented and Virtual Reality: Remote Sensing Tools for Insurance Value Chain

Health and Insurance: Health Informatics, Internet of Things, and Integrated Electronic Health Records

Integrated Insurance Ecosystem: The Next Generation Insurer

Data as the Foundation for Insurance User Experience

The Internet of Things and Property/Casualty Insurance

The Internet of Things and Life & Health Insurance

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