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Big Data

Last Updated 7/12/2018

Issue: As insurers collect more granular data about insurance consumers, state insurance regulators need greater insight into what data is available to the industry, how it is being used, and whether it should be used by insurers. While the use of big data can aid insurers’ underwriting, rating, marketing, and claim settlement practices, the challenge for insurance regulators is to examine whether it is beneficial or harmful to consumers. Additional consumer concerns include how collected data is safeguarded and how consumer privacy is maintained. Another issue with big data is state insurance regulators need data beyond what has been traditionally collected. State insurance regulators may need to collect more useful data (beyond financial and market conduct data collected today) to allow for greater insight into insurers’ models to further enhance regulation.

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 is currently focused on the use of data for accelerated underwriting in life insurance. It is also continuing to review of the current regulatory framework for the oversight of insurers' use and need of consumer data. This includes analysis on the skills and resources required for the NAIC to review predictive models and confidential procedures for sharing predictive modeling information. Additionally, the Working Group is monitoring the Property and Casualty Insurance (C) Committee's consideration of additional charges for the Casualty Actuarial and Statistical (C) Task Force to 1) propose revisions to the Product Filing Review Handbook to include best practices for review of predictive models and analytics filed by insurers to justify rates; 2) draft and propose state guidance for rate filings based on complex predictive models; and 3) facilitate training through predictive analytics webinars.

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.