By Stuart Rose, Global Insurance Marketing Director, SAS
Cary, N.C. (Oct. 1, 2013) – There are many things that you could do in just over 10 weeks:
- If you are Kris Humphries and Kim Kardashian, you could get married and file for divorce.
- If you are Brian Clough, you could be appointed and 44 days later sacked as manager of English soccer team Leeds United.
- If you are Phileas Fogg, you could nearly travel around the world.
If you are a life insurance company, you could issue a life insurance policy!
Amazingly, the average time from the start of the underwriting process to policy issuance for a mid-to high value life insurance policy is 72 days. With life insurance sales at a 50-year low, insurers need to find ways to make it easier to issue policies.
One of the most innovative ways that insurers are changing the underwriting process is by creating analytical models to predict whether further medical tests are needed. The underwriters use these predictive models as a guideline for ordering additional tests. If no tests are needed, the policy can be quickly issued. The models are developed using third-party consumer data sources to identify risky attributes.
Premium growth is an equally important part of the value proposition for implementing underwriting analytics. Common sense says that the longer it takes to underwrite and issue a policy, the higher the non-take up rate. Reducing the underwriting process decreases the non-take up rate and increases new business sales.
In a highly competitive market, speed to issue is a significant differentiator. To enable this differentiator, insurance companies need to move analytics from a back-office function, used by actuaries and other statisticians, into a real-time environment. By embedding analytical models into transactional systems, like policy administration applications, insurers can support real-time underwriting decisions and make their organization more attractive to agents and producers, and hence drive revenue growth.
To find out more on how insurers are capitalizing on the opportunities presented by predictive modeling within insurance download a copy of the research paper “Operationalizing Analytics: The true state of predictive analytics in insurance.”
About the Author
Stuart Rose is global insurance marketing director at Cary, N.C.-based SAS. Rose, a 25-year veteran of the insurance industry, 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.
Stuart is a regular contributor to insurance publications and the Analytic Insurer blog, as well as co-author of the book Executive Guide to Solvency II. He frequently speaks at insurance conferences.
SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 60,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know®.