Why 2019 is the year of better data analytics for risk management

Why 2019 is the year of better data analytics for risk management | Insurance Business

Why 2019 is the year of better data analytics for risk management

In the second of three guest posts highlighting predictions for the insurance industry in 2019 , Kate Sampson, Metabiota advisory board member and insurtech advisor/expert and the first vice president of risk solutions at Lyft highlights why with the continued increase in the use of data and analytics across all of the insurance ecosystem, particularly as it relates to the underwriting process, companies in the year ahead will rely more heavily on more real-time data to make more informed decisions and more robust policies/solutions.

As a result, she believes companies will evolve and tap into better and more timely insights to make more informed decisions. Which is why 2019 is the year of better data analytics….

The increase in the amount and access to new and richer data is sparking transformational change in the insurance industry. Between the sheer volume of data and rapid advances in data science, AI and machine learning, this information revolution is contributing to a significant growth in new insights and understanding about private and public sector vulnerabilities to perils. Particularly over the last decade, we have seen the industry take vast amounts of structured “historical” data and apply new algorithms and predictive models to improve underwriting results. However, we are still in the early stages of the industry using “real-time” and unstructured data in the underwriting, product development, claims and risk management processes.

Where is the industry with the next step of this transformation?

  • Telematics – Many insurers are using telematics-derived, driving data to understand driving behaviors of insureds. This data is allowing insurers to adapt their underwriting process and develop new products. Insureds are also using telematics data to identify development areas for drivers in their fleet, using the data to inform driver-training programs. The use of real-time telematics data in the claims process and dispatch algorithms is still developing.  As this technology continues to advance, we could see expedited claims resolution and further improvement in creating safer roads.
  • Connected Devices – From smart phones, smart homes and smart buildings, connected devices have the potential to be one of the most powerful risk mitigation tools introduced this decade. Connected devices that provide early detection of possible unsafe conditions can trigger a number of potential actions to mitigate a loss. Many insurers are providing “credits” for insureds with these devices, be it in their warehouse or within their workforce.  Use of this technology on a real-time basis can further inform the actuarial and underwriting process, allowing underwriters to provide credit for improvement in loss experience throughout the policy term as opposed to waiting 12 months (or more) to analyze loss data.  
  • Imaging Tools – High resolution imagery now available from satellites, aircraft and drones are creating efficiencies in the underwriting process, allowing underwriters a wealth of additional information of an insured’s property footprint. Risk managers are deploying these technologies in their risk management process, for easier monitoring of potential property maintenance issues. Real-time use of these technologies is positioned to drastically change the claims process. Claims teams can further embrace imaging data into the claims process increasing both speed and accuracy or claims adjusting. As this technology continues to develop, we should expect insurers to re-evaluate pricing models to incorporate the risk mitigation and the reduction in value of claims from this technology.
  • Emerging Risks Models – Advances in sophisticated models analyzing a company’s exposure to cyber, pandemic and fraud risk are allowing a company to better understand, mitigate and finance these emerging risks. The evolving data streams informing these models can also be leveraged by insurers and insureds. This data aids the insurers in creation of new products or to iterate existing products. One such example is the product, PathogenRX, co-developed by Marsh, Metabiota and Munich Re. This risk transfer solution uses historic event catalogs, frequency and severity analytics, pathogen libraries, and human sentiment data to inform a product designed to provide non-damage business interruption to industry sectors prone to economic losses stemming from infectious disease epidemics. Insureds, such as hotels and convention centers, can use this data to evaluate their exposure to these risks and evaluate risk financing options.

In many cases, the most relevant real-time data actually resides in the hands of the consumer of insurance. Now more than ever, risk managers have access to vast amounts of internal data – much of which is not part of the typical underwriting process.  This data provides the insured with numerous advantages:

  • Better Identification of cost drivers - Richer data provides a more granular real-time view of risk areas in their organization and the changing profile of that risk. Risk managers will be provided with better data, enabling consideration of the cost of a particular risk and various strategic initiatives.
  • Better Identification of cost drivers - Richer data provides a more granular real-time view of risk areas in their organization and the changing profile of that risk.  Risk managers will be provided with better data, enabling better evaluation of the cost of a particular risk when considering various strategic initiatives.  
  • Demand for product innovation – It is likely risk managers can gain insights from their internally generated real-time data faster than insurers can adapt products to respond. This will lead to increased demand for nimble insurance products that can react to a company’s changing risk profile or more episodic risks in their business.
  • Expectations for increased efficiency – As more granular underwriting data will exist in digital formats of insureds, the insurers will need to have the ability to work with more sophisticated data sets provided by insureds than a typical excel spreadsheet provided at renewal. Expect to see a rise in the use of APIs in the renewal process, particularly affinity/program type business.

If risk managers and consumers are able to leverage real-time data faster than the insurers can respond with their underwriting and product development – it is reasonable to expect that insurers will be misaligned with consumer needs. If insurers are unable to digest real-time data to inform underwriting and pricing models, it is reasonable to expect a rise in consumers evaluating alternative risk financing approaches, utilization of captive insurers and direct access to nimble reinsurance and alternative capital markets. The time is now for insurers to accelerate the adoption of these advances in technology, information digestion and analytics in order to design products that are flexible, customized and valued by the insured.

The above was an opinion piece written by Kate Sampson, insurtech advisor/expert and the first vice president of risk solutions at Lyft and advisory board member for Metabiota. The views expressed within the article are not necessarily those of Corporate Risk and Insurance.