The insurance industry has always made use of data to make better decisions. The underwriting department of an insurance company has been at the forefront of data use for many years – using historical claims data as well as factors such as driver age, gender, credit score, and so on to project potential for loss, and make risk determination.

Now, many insurers are making significant investments in predictive analytics. According to the IVANS 2011 Carrier Automation Trends study released September 2011, 37 percent of carriers are currently using or plan to integrate predictive modeling and business intelligence into their organizations in the next 12 months, with the goal of greater consistency and accuracy in business decisions in less time.^{^[i]}

A survey conducted by Towers Watson in October-November 2011 specifically asked insurance carriers about the bottom- and top-line benefits of incorporating predictive models into rating, pricing, underwriting, and risk selection processes. Seventy-five percent of those surveyed identified bottom-line benefits of rate accuracy, loss ratio improvement, and improved profitability. Over one-third of those surveyed also identified top-line benefits of 'expansion of underwriting appetite', 'improved renewal retention,' and increased market share.[ii]

How Can Analytics Help in Claims?

The average consumer today has an auto accident once every seven to 10 years, well behind the average vehicle trade-in cycle of every five years. Due to the nature of the product being sold, auto insurers typically have very limited interaction with consumers outside of policy issue, bill time, and sometimes through other services such as banking However, the real moment of truth for the insurer is at the time of an accident, making claim's handling one of the most significant opportunities for an insurer to retain or lose a customer.

The ability for insurance carriers to advance their use of analytics to improve the customer experience is becoming a must, particularly as consumers demand that the claims experience be as satisfactory as with any other interaction they have with companies they frequent.

Many claims organizations have made good use of analytics to help track performance and improve collaboration with business partners, including:

The use of electronic appraisal reviews and shared guidelines have helped to ensure business partners have the information needed to fulfill work in a transparent, compliant, and complete manner.
The use of management dashboards have helped facilitate claims performance reviews in a concise, targeted manner, enabling managers to address specific areas of performance, adjust levers, and evaluate the impact in real time.

These tools are necessary and valuable, but they're the tip of the iceberg. Advancements in technology have enabled the next wave of analytical tools that lead to more actionable information and less reliance on human interpretation of data. Today's tools also work to more directly enhance the customer experience.

What's the Problem?

Two big challenges remain in today's claims process: slow total loss identification and inefficient workflows, beginning right at first notice of loss (FNOL).

To identify total losses, insurance carriers have historically used a series of questions, or a 'decision-tree' method to determine whether a vehicle is repairable or a total loss at FNOL. This is problematic as many total losses aren't identified at FNOL and are advanced through the claims process – inspections are conducted and estimates written – before a formal vehicle valuation is requested and, only then are a majority of total losses identified. These extra steps can increase claims-related expenses, including salvage, tow and rental; increase cycle time, and negatively impact customer satisfaction.

Another challenge for insurance carriers is efficient and cost-effective claims routing. Currently, there is no systematic way to quickly assess and project the value of a claim at FNOL. For example, to send a staff appraiser to inspect a vehicle out in the field may not be a cost-effective approach if the vehicle repair cost is $1000 or less.

Claims Gets Proactive

CCC has developed a predictive model that builds on work it began over three decades ago to create electronic processes for insurance claims professionals and their business partners. CCC ONE™ Predictive Solutions is an analytics tool that recommends routing damaged vehicles by applying estimated vehicle repair cost data, vehicle market values and estimated salvage costs using guidelines established by insurance carriers. Within minutes, FNOL representatives using the CCC solution can route totaled vehicles for salvage, while repairable claims are returned with recommendations for routing to the appropriate appraisal source.

CCC's automotive insurance claims domain expertise uniquely positions the company to deliver the CCC ONE Predictive Solution.

CCC ONE™ Predictive Solutions – Early Results are In

Early results from a series of insurance carrier pilots show benefits within two key areas: early total loss identification and cost-efficient routing of claims to optimal appraisal sources. With total losses, insurance carriers in the pilots have seen, on average, a 50 percent improvement in their ability to determine when a claim will exceed their established total loss thresholds, while staying within a range of 1 to 4 percent of false positives. This helps insurance carriers to more quickly notify their customers of total losses and realize savings in towing and salvage costs.

CCC ONE Predictive Solutions pilots improved insurance carriers' ability to predict low-value claims (based on their own guidelines) by 80 percent, improving their ability to route the claim to the appropriate appraisal source.

To learn more about CCC ONE Predictive Solutions, please visit: www.cccis.com

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Improve Claims Performance with Better Information, Earlier

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