Big data could be used by insurers to refine classes of risk & pricing

Big data could be used by insurers to further refine classes of risk and determine better pricing and availability of insurance coverage to ensure a better match for policy owners, according to the Canadian Institute of Actuaries (CIA).

Using big data derived from new technologies can contribute to the healthy functioning of insurance markets.

Thanks to big data analytics, organizations can now use that information to rapidly improve the way they work, think, and provide value to their customers. With the assistance of tools and applications, big data can help you gain insights, optimize operations, and predict future outcomes.

The use of big data is appropriate in insurance ratemaking, and that access to such data creates improved insight about risk and its contributing factors.

Restricting access to this data could adversely impact the availability or price of insurance for individuals, the CIA added.

As big data becomes increasingly available through new technologies, insurers can use it to further refine their classes of risks and offer insurance that is more aligned with the different needs and situations of policy owners

Matthew Buchalter, FCIA

Access to more data means insurance ratemaking can be based on more appropriate factors, ultimately reducing risk and setting more refined insurance costs.

Actuaries stress that big data – like all data used in ratemaking – is subject to the ethical data collection practices, privacy laws, and information security requirements necessary to protect consumers.

How Big Data analytics works? Analytics solutions glean insights and predict outcomes by analyzing data sets. However, in order for the data to be successfully analyzed, it must first be stored, organized, and cleaned by a series of applications in an integrated, step-by-step preparation process:

  • Collect. The data, which comes in structured, semi-structured, and unstructured forms, is collected from multiple sources across web, mobile, and the cloud. It is then stored in a repository—a data lake or data warehouse—in preparation to be processed.
  • Process. During the processing phase, the stored data is verified, sorted, and filtered, which prepares it for further use and improves the performance of queries.
  • Scrub. After processing, the data is then scrubbed. Conflicts, redundancies, invalid or incomplete fields, and formatting errors within the data set are corrected and cleaned.
  • Analyze. The data is now ready to be analyzed. Analyzing big data is accomplished through tools and technologies such as data mining, AI, predictive analytics, machine learning, and statistical analysis, which help define and predict patterns and behaviors in the data.

by Peter Sonner