Filed Under:Technology, Analytics & Data

The Analytical Insurer

Analytics is certainly not new to the insurance industry. It could be argued that the first mortality tables developed in the 18th century were analytics. However, the adoption rate of business analytics within insurance has been slow.

Today, predictive modeling and forecasting are used by actuaries for pricing, but few insurance companies have applied analytics in a real-time or near-real-time operational environment.

The “analytical insurer” is an insurance company using analytics throughout its organization to improve business performance. It has four major components:

  • Claims analytics
  • Customer analytics
  • Channel analytics
  • Product analytics

Claims analytics

Claims are by far the biggest expenses within a p&c insurance company and can account for up to 80 percent of an insurer’s revenue. The way an insurance company manages the claims process is fundamental to its profits and long-term sustainability.

Claims analytics is the process of analyzing the structured and unstructured data (i.e., email, adjuster notes, medical records or police reports) at all stages in the claims cycle (FNOL to payout to subrogation). Since up to three-quarters of claims data is unstructured data, the ability to analyze it is essential to improving the claims cycle. 

Claims fraud is already a widespread problem for insurers, and in a difficult economy it tends to accelerate. The most effective way to combat both opportunistic and organized claims fraud is to use a combination of business rules, predictive modeling, anomaly detection and social network analysis. Using analytics will not only detect, but also prevent fraud before claims are paid.

Insurers often only receive a fraction of not-at-fault settlement costs because they don’t pursue subrogation opportunities. Using claims analytics, insurance companies can identify known subrogation characteristics and optimize associated activities.

Finally, some insurers are beginning to use analytics to calculate a litigation propensity score. Claims that involve an attorney often double the settlement amount and significantly increase an insurer’s expenses. Analytics can help insurers determine which claims are likely to result in litigation and assign those claims to more senior adjusters, who can settle the claims sooner and for lower amounts.

Customer analytics

With little differentiation between product offerings, it is extremely challenging for insurance companies to retain customers, resulting in poor loyalty levels and increased acquisition costs.

Customer analytics is the ability to segment customers according to their likely buying behavior and potential profitability.

As the cost of acquiring new customers continues to rise, insurance companies are using analytics to develop customer retention programs. Data mining techniques can be used to predict the likelihood that a policyholder may not renew or lapse his or her policies. By proactively using this information, insurers can create marketing campaigns to prevent policy cancellation.

Another concept that insurance companies are beginning to embrace is the use of customer lifetime value. Insurance companies calculate the net present value of the customer based over an extended period, say five to 10 years. Knowing the lifetime value of a customer is a benchmark for companies on how much they would or should be willing to invest to acquire/retain a customer.

Channel analytics

The way that people buy insurance is rapidly changing. As insurance companies begin to implement multichannel integration strategies, the challenge for the insurance company is to determine the right distribution method for each customer.

Channel management is certainly not a new concept; however, management decisions are driven on historical, periodic performance reporting. Product managers and field marketing can only react once an agent persistency rate or other KPIs fall below an unacceptable rate, well after corrective action should have been taken. Channel analytics help to conduct “deep dives” into causal factors, which in turn require access to predictive data and forward-looking analyses.

Product analytics

In many lines of business, insurance has become a commodity with customers often choosing insurers purely on the basis of price. Product analytics is the ability to analyze the impact on profitability when there are new products and changes to existing pricing structures.

Product pricing or ratemaking is undergoing a significant transformation. Insurers are increasing their use of advanced analytical tools, like generalized linear modeling, to get more granular rates. As insurers capture more and more data, the more they will learn and predict – delivering better results. In fact, some insurance rates will become very personalized as insurers begin to use data from in-car computers, weather patterns and even road conditions to determine premiums. 

In addition, insurers are building analytical models that analyze the impact of proposed rate changes on policyholder retention and conversion rates for an existing book of business. 

In a highly competitive market, it is vital for insurance companies to minimize inefficiencies and reduce losses to protect profitability. By using data proactively, companies can better understand their business, detect areas for improvement and take remedial action. Analytics has become fundamental for insurers to remain competitive. 

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