To fully exploit business analytics, carriers must supplement their internal policy-level data with a variety of data points derived from external sources. To get a sense of how insurers can best harmonize exogenous data with existing business processes, Insurance Networking News checked in with Bill Madison, SVP insurance data solutions for Lexis/Nexis.
INN: How can insurers better incorporate advanced customer data into operations?
BM: While insurers currently have access to and often use customer data, e.g., public records, they use it reactively, assessing risk in underwriting processes or paying potentially fraudulent claims. Insurers need a proactive data analytics approach applied to their operations, taking full advantage of multiple data sources and analytics engines from underwriting to claims as well as life and other markets.
By proactively using data throughout the entire customer underwriting or claim lifecycle, you can better predict risk and determine the best workflow. For example, insurers can use analytics to determine low- or no-touch processing of a claim for payment versus sending it to the SIU or ensuring your experienced adjusters or internal experts, e.g. medical, are involved in processing specific claims. As consumers move to shopping online, insurers must balance speed and ease of use with discipline in underwriting. Advanced data enables carriers to verify their customers' information, not just at the point of quote, but throughout the customer relationship as risk profiles change over time. Better use of data also brings more consistent internal processes across the organization, which leads to better protection of a carrier's brand and position in the market.
INN: What technologies need to be in place for insurers to apply these sources of data for analytics?
BM: Today's modern systems offer easy integration of both internal and external data for analytics. Some technology solution providers are able to offer "actionable" data and analytics across most platforms-internally and externally built. Actionable data means we can take a number of data attributes across many different sources and deliver it in a fashion that can be easily integrated (i.e., scoring) into any platform.
Today we are in a position to take "big data" down to a level of granularity that enables easier execution into any technology platform. Platforms should enable the integration of predictive modeling, data request, data pre-fill, identity matching and better use of public records data. We are seeing more benefits and interest in relationship analytics for underwriting and gauging fraud.
INN: Should insurers target certain areas of the enterprise for new initiatives or is a more holistic approach preferable?
BM: If insurers are using proactive analytics in other parts of their businesses, e.g., underwriting, we recommend expanding to develop a holistic analytics program, a dynamic, data-driven approach of continually evaluating risk as more information about the risk becomes available.
To reap the full benefits, carriers must supplement their internal policy-level data with a host of external data. Using external data only in specific areas of the insurance workflow is really becoming a thing of the past; we see a developing best practice where carriers are implementing a holistic, proactive analytics approach across the organization and throughout the workflow. The result is an improved lifecycle: one that is cost- and time-efficient, effectively minimizes risk and fraud loss opportunities, creates consistency, improves process workflows and enhances customer service.
INN: How can underwriting data be leveraged for claims management?
BM: A deeper understanding of consumer data used by underwriters helps determine risk, which can then be used to make decisions about the best way to triage claims. Some of the same internal data utilized to help validate consumer information and underwrite their policies can and should be used for claims management to again assess risk and determine the best process for each claim. Using this available data helps insurers more efficiently address their claims with a consistent discipline, automating processes from the beginning.
Underwriters use data to better understand risks in particular households, which should also be used in claims management to better understand both the history and the new, changing dynamics of households.
We've made great advancements in understanding and knowing more about the customer, whether business or consumer; commercial or personal, but there's a tremendous road ahead. Insurers must take advantage of this knowledge to assess risk because the future leads to insurers making money through better underwriting and reduction in claims.
