December 2009

This Issue: Predictive Analytics

Contents:

• Analytics in Action: The Past, the Present, and the Promise
  By Frank J. Coyne
  ISO's CEO looks at how competition — fueled by sophisticated predictive analytics — is driving consolidation in the insurance marketplace. But companies large and small can use technology to keep pace with change and succeed in our challenging business environment.
• Managing Data for Analytics
  By Phil Hatfield
  The foundation of a successful analytics operation is quality data and superior data management. Predictive analytics is a unique use of data, requiring special procedures for data management and special skills, training, and experience for the people doing the work.
• Benchmarking Analytics for More Reliable Exposure Data and Catastrophe Modeling
  By Uday Virkud, P.E., and George Davis, FCAS, MAAA
  To get reliable results from a catastrophe model, you need high-quality exposure data. Comparing your company data against industry benchmarks can help you improve your quality and fine-tune your data collection and data management practices.
• More Accurate Loss Reserving May Speed Housing Market Recovery
  By John Leamons and Cecil Rhodes
  Private mortgage insurance is vital to the nation's housing market, and the way mortgage insurers calculate their loss reserves will have an important effect on the amount of new business they can write. More accurate analytic methods can benefit insurers, their customers, and the general economy.
• The Value and Scope of Predictive Analytics
  Interview with Marty Ellingsworth, President of ISO Innovative Analytics (IIA)
  Marty Ellingsworth, president of ISO Innovative Analytics, speaks with ISO Review about why and how insurers should use predictive analytics to manage the risk in their books of business.
• Third-Party Data for Analytic Solutions: A Road Map from Identification to Implementation
  By Darlene Pogrebinsky
  Integrating data from outside sources into your predictive models can give you meaningful competitive advantages. How can you make sure you select the right data and incorporate it in the most effective, efficient ways?
• Advanced Analytics Can Improve Customer Retention
  By Raj Bhat and Sanjiv Mishra
  Attracting and retaining the right customers should be the goal of every insurance company. Today's advanced analytics, combined with regular customer contact, makes it easier than ever to meet that goal.
• Climate Change Analytics: Limitations and Opportunities
  By Ross N. Hoffman, Peter S. Dailey, and Michael R. Murray
  There is still substantial uncertainty about the causes, pace, and implications of climate change. But if we put off managing the risks until we know all the answers, we may one day pass a tipping point and find it is too late to prevent disaster.
• Healthcare Analytics for National Challenges: The German Experience
  By Alec McLure
  The German government is now using sophisticated analytics to manage the fair distribution of healthcare resources among diverse patient populations. In the United States and around the world, analytics is becoming increasingly necessary to help solve the fundamental problems of healthcare systems.
• ISO Review Forum:
  Analytical Opportunities across the Property/Casualty Life Cycle
  In this ISO Review discussion forum, four ISO commentators show how a hypothetical multiline insurer could use analytical techniques to achieve growth and sustain productivity in the strategic areas of marketing, underwriting, pricing, and operations.
  • Applying Advanced Analytics to Marketing Strategies
    By Karthik Balakrishnan
  • Using Advanced Analytics for Improved Risk Understanding and Underwriting
    By Rob Curry
  • Insurers Can Augment Traditional Ratemaking with Advanced Predictive Modeling
    By Mark Richards
  • Applying Operations Analytics to Improve the Claim Settlement Process
    By Janine Johnson
• Data Quality for Analytics
  By Gerry Gloskin
  What metrics can you use to evaluate the quality of data for predictive modeling? Start with accuracy, reliability, timeliness, and completeness.
• Analytics Boosts Claim Consistency and Helps Manage Claim Costs
  By Rich Della Rocca
  Adjusting insurance claims is a complicated business, and insurers often find it difficult to achieve consistency in settlements. Information-driven claims management tools can help.
• Using Data Proactively: Drill-Down Reporting vs. Predictive Analytics
  By David Cummings
  A centralized data warehouse is a tremendous asset for any company. But how you use that asset is the key to its value for your enterprise. Do you simply go on scavenger hunts through the data? Or do you use predictive analytics to find insight?
• Analytics in Catastrophe Management
  By KC Agrelius
  After a catastrophe, claims managers have just a small window of time to make decisions that can affect their company's long-term financial stability. Analytical tools and real-time information can extend the reach and effectiveness of claims professionals by helping them focus where the need is greatest.
• Efficiency and Value: The Promise of Predictive Analytics Technology for P/C Insurance
  By Ara C.Trembly
  Some have worried that predictive analytics will take away the jobs of underwriters, actuaries, and claims professionals. The well-known consultant, journalist, and lecturer Ara Trembly argues that just the opposite is true.
• Using Predictive Analytics to Optimize the Premium Audit Process
  By Sharon Carney
  Using predictive modeling, premium audit teams can identify policies with particular characteristics and find hidden premium. Analytics can increase investment income, boost the bottom line, and improve the value of the premium audit function.
• ISO Review Industry Roundtable
  John Lucker, Claudine Modlin, Doug Winter
  Executives from Deloitte Consulting LLP, EMB America, and Accenture share their views on new predictive analytics and analyses, opportunities for improvement, early adoption of predictive analytics, and obstacles to overcome.
• Adopting Predictive Modeling for Competitive Advantage
  By Glenn Meyers
  Early adopters of predictive modeling can gain dramatic competitive advantage. What questions do you need to consider when evaluating a predictive modeling project for insurance underwriting?
• The Challenges of Leveraging Analytics in the Property/Casualty Industry
  by Dale Halon
  It's hard. It's expensive. It takes away from other priorities. But those who see the advantages they can gain by adopting analytic tools and procedures are years ahead of the competition.
• The Optimized Insurer
  By Stuart Rose
  The global insurance marketing manager at SAS argues that the "optimized insurer" is a company that uses analytics throughout its organization to improve business performance. The optimized insurer takes advantage of claims analytics, customer analytics, channel analytics, and product analytics.
• Probable Maximum Loss Considerations in Commercial Fire Insurance Underwriting: An ISO Perspective
  By William Raichle and John Vorbeck
  Analysis of Probable Maximum Loss — the largest loss a commercial building is likely to suffer in a single fire if all existing mitigation features work properly — can help you with underwriting and reinsurance decisions.
• ERM: The Need for a Data-Driven Approach
  By Joe Palmer
  Some say that enterprise risk management is simply the best practices that effective risk managers have followed for years. But ERM benefits from a data-driven approach, including mathematical simulation of unlikely, yet potentially critical, loss events.

»ISO REVIEW Archive

del.icio.us