5 Ways to Optimize Insurance Pricing Efficiently

How do you price a product without knowing production costs? The insurance industry faces this problem every day. While most industries know the cost of materials, labor and profit margin to calculate the price of their products, insurers do not know the cost of the product when it is sold. The true product cost may not be known for many years, until claims are paid. Therefore insurance companies – specifically actuaries – rely heavily on historical data to predict future behavior for premium rate creation so they can price products.

Competition is forcing insurers to adjust rates more frequently to retain existing customers and attract new ones. Yet many insurers take weeks, if not months, to implement a new rating structure, and the effective performance of these models rapidly deteriorates over time. Inevitably, insurance is changing its approach with regards to product pricing. As insurance becomes more and more of a commodity, insurance companies are trying to differentiate themselves from their competitors based on customer services, claims experience and financial strength, but mostly by price. Thus, to gain a competitive advantage, insurers are beginning to use price optimization.

Price optimization goes beyond the traditional insurance ratemaking process with sophisticated methods such as predictive analytical models, customer lifetime value calculations and scenario simulation to increase rating accuracy and improve profitability. Although the concept of price optimization is relatively new to the insurance industry, it has been used in other industries, such as travel and retail for a number of years.

Regulations, lack of reliable IT tools and even limited online presence are often mentioned as reasons for its late adoption within insurance. However, relaxed regulations and the growth of aggregator websites means that price optimization is becoming more of a reality.

Insurers looking to implement a price optimization strategy must consider these essential components:

• Information management. Key to the success of using price optimization is the quantity and quality of the available data, especially claims and customer data.

• Data exploration. The emergence of business analytics software, like data exploration and visualization tools, helps insurers refine their analysis and evaluation of certain risk elements. For example, 20 years ago, credit score was probably deemed unimportant. Now it is probably the most used variable in determining premium rates.

• Predictive modeling. Insurers must use analytical tools to perform what-if simulation and scenario testing to forecast future behavior and improve the underwriting performance of the insurance company.

• High-performance analytics. To process the large data quantity and perform complex analytical calculations, insurers need an in-memory or distributed computing environment.

• Competitive intelligence. Price optimization requires an in-depth understanding of the competitive landscape, industrywide pricing strategies, and customer demographics and buying preferences.

Especially in lines of business where price is a key differentiator – such as auto, home and some commercial lines – price optimization represents the future for insurance.

About the Author: Stuart Rose is global insurance marketing manager at Cary, N.C.-based SAS. Rose is a 20-year veteran of the insurance industry who began his career as an actuary. He has worked for a global insurance carrier in both its life and property divisions and has worked for several software vendors, where he was responsible for marketing, product management and application development. He has driven successful development and implementation of enterprise systems with insurance companies in the U.S., the U.K., South Africa and Continental Europe.