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Eric Nordman
Director, Center for Insurance Policy & Research
816-783-8232

CIPR Staff

Big Data

Last Updated 10/02/17

Issue: Big data is disrupting the insurance sector and will dramatically affect the way it looks in the next decade. Insurers' use of big data has changed the way they market, price and process claims, as well as their approach to risk management. These changes can be beneficial or detrimental to both consumers and insurers. Properly used, big data can enhance customer engagement and provide insurers with a competitive advantage. However, it also has the potential to create consumer protection concerns. Additionally, the complexity inherent in synthesizing huge amounts of digital data brings the need for enhanced insurance regulatory systems.

Background: The digital revolution has allowed for the collection and storage of large and diverse amounts of information. This data is referred to as big data because it is too complex for traditional data processing techniques. For insurance purposes, big data refers to unstructured and/or structured data being used to influence underwriting, rating, pricing, forms, marketing and claims handling. Structured data refers to data in tables and defined fields. Unstructured data, comprising most data, refers to things such as social media postings, typed reports and recorded interviews. Predictive analytics allows insurers to use big data to forecast future events. The process uses a number of techniques—including data mining, statistical modeling and machine learning—in its forecasts.

Insurers use big data in a number of ways. Insurers can use it to:

  • More accurately underwrite, price risk and incentivize risk reduction. Telematics, for example, allows insurers to collect real-time driver behavior data and combine it with premium and loss data to provide premium discounts.
  • Enrich customer experience by quickly resolving service issues.
  • Improve marketing effectiveness by tailoring products to individual preferences.
  • Create operating efficiencies by streamlining the application process. An example of this is a pre-filled homeowners application.
  • Facilitate better claims processing by applying machine learning algorithms to outcomes.
  • Reduce fraud through better identification techniques. For example, text analytics can identify potential "red flag" trends across adjusters' reports.
  • Improve solvency through the ability to more accurately assess risk.

Big data has tremendous potential to positively affect insurers and consumers. However, all disruptive technologies bring challenges. Big data concerns include:

  • Complexity and volume of data may present hurdles for smaller-sized insurers.
  • Insurance regulatory resources for reviewing complex rate filings.
  • Lack of transparency and potential for bias in the algorithms used to synthesize big data.
  • Highly individualized rates that lose the benefit of risk pooling.
  • Collection of information sensitive to consumers' privacy or potentially discriminatory.
  • Cyberthreats to stored data.

 
Status: As stated earlier, the age of big data brings both positive and negative impacts to society. The job of state insurance regulators is to ensure regulations and regulatory activities sufficiently protect consumers from harm. To assist with this, the NAIC created the Big Data (EX) Working Group of the Innovation and Technology (EX) Task Force. The Working Group held a public hearing during the 2016 NAIC Spring National Meeting. The hearing was the first step in obtaining a broad understanding of how big data is being used in the insurance industry, the impacts on consumers and how state insurance regulators can make use of it.

In 2017, the Working Group is reviewing current regulatory frameworks used to oversee insurers' use of consumer and non-insurance data for possible revision. The initial focus is on insurers' use of data for rating and claims in personal lines property/casualty (P/C) insurance. It also will assess data needs and required tools for state insurance regulators to appropriately monitor the marketplace and evaluate underwriting, rating, claims and marketing practices. Additionally, the Working Group is developing a proposal to address the need for the state sharing of resources to review complex models used in rate filings. The aim is to eliminate duplication of effort.

Sources: Big Data (D) Working Group Dec. 10, 2016, minutes accessed at www.naic.org/meetings1704/cmte_ex_bdwg_2017_spring_nm_materials.pdf and May 19, 2016, minutes accessed at www.naic.org/meetings1608/committees_d_big_data_wg_2016_summer_nm_materials.pdf.