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High Tech Processing: From Application to Policy Issue

High Tech Processing: From Application to Policy Issue. Presented by Keith Hoeffner February 16, 2011. Agenda. High Tech Processing – present challenges Electronification of application fulfillment Wide Open Possibilities Available Now What’s next?. Process Challenges.

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High Tech Processing: From Application to Policy Issue

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  1. High Tech Processing: From Application to Policy Issue Presented by Keith Hoeffner February 16, 2011

  2. Agenda • High Tech Processing – present challenges • Electronification of application fulfillment • Wide Open Possibilities Available Now • What’s next?

  3. Process Challenges • Obtaining a complete and legible application • Part 1 • Part 2 • Cycle Time • Paramedical exam and lab • EKG scan • APS • Piece meal delivery • Discretionary requirements • 30+ days • Legal and compliance adoption of process improvements

  4. Life Insurance Application Process COMPLICATED

  5. Don’t Be Trapped In A Paradigm

  6. Wide Open Possibilities • Straight through processing • Plus data mining • Real-time transactions • Workflow improvements • Predictive modeling

  7. Straight Through Processing End-to-End Life Insurance Application Workflow Reduces Cycle Time by 14+ Days

  8. What do we do with the data? • Automated underwriting • Import application data directly into underwriting system – eliminate data entry • Workflow tools and business rules order medical requirements • Rules based decisions • Routing of more complex cases to the right underwriter at the right time

  9. Paving the Cow Path • Nothing wrong with paving the cow path when the cow path indicates a desire line that leads to process efficiency. • Until you are ready for the super highway

  10. How Do You Make a Difference? Stage 1 • Integrate external data into straight through process • Prescription history • MIB • MVR • Eliminate contradictions • Take an underwriting file from IGO to IRGO In REALLY Good Order • How?

  11. The Advent of Real-Time Transactions • Real-time transactions are made possible through Web Services – a method of communication between two electronic devices over the web • Web services describes a standardized way of integrating Web-based applications using • UDDI to list the services • WSDL to describe the services • SOAP to transfer the data over the Internet • XML to tag the data

  12. Real-Time Transactions • Web services • Used primarily as a means for businesses to communicate with each other and with clients • Web services allow organizations to communicate data without intimate knowledge of each other's IT systems behind the firewall • Web services allow different applications from different sources to communicate with each other without time-consuming custom coding • Because all communication is in XML, Web services are not tied to any one operating system or programming language

  13. Real-Time Transactions • Web services (continued) • Java can talk with Perl, Windows applications can talk with UNIX applications, etc. • Web services do not require the use of browsers or HTML • Web services are sometimes called application services

  14. How Do You Make More of a Difference? Stage 2 • Process improvements • Expand the data set • New field technology to capture more data • Digital ECG’s • Laptop • Improve workflow • Real-time exam scheduling • Voice signatures and e-signatures • Laptop and call center integration

  15. How Do You Really Make a Difference?Stage 3 • Predictive modeling – the next step beyond automated underwriting • What is predictive modeling? • Predictive modeling is the process by which a model is created or chosen to try to best predict the probability of an outcome • In many cases the model is chosen on the basis of detection theory to try to guess the probability of an outcome given a set amount of input data • Discerning between information bearing data and noise

  16. Look very closely at the next animated slide…

  17. Which way was the woman whirling?

  18. How To Take It To The Next Level • MIB, prescription history, MVR • Relevant lifestyle data • Exercise • Diet • Demographic: population density, medical care index • Personal: gender, age, occupation, education, marital status • Finances: assets, income, credit history • How do you mine this data?

  19. Consumer Data – Grocery Loyalty Card • Age and gender • Tobacco use • Alcohol use • Occupation • Neighborhood • Hobbies and interests • ATM use (noise or informational data) • Brands (or noise or more informational data)

  20. What Do You Do With It? • Correlations? Cause and effect? • Sea temperatures and hurricane frequency • Education and earnings • Height and weight • Marital status and mortality • Type of neighborhood and longevity • Lifestyle and mortality Predictive Underwriting – Paul Hately, Swiss Re

  21. Predictive Underwriting – Paul Hately, Swiss Re

  22. Maybe I’m just not smart enough to figure all this out. Are you?

  23. Olny srmat poelpe can  raed this.  I cdnuolt blveiee that I cluod aulaclty  uesdnatnrd  what I was rdanieg. The phaonmneal pweor of the hmuan mnid, aoccdrnig   to a rscheearch at Cmabrigde Uinervtisy, it deosn't mttaer in what  oredr the ltteers in a word are, the olny iprmoatnt tihng is that the  first and last ltteer be in the rghit pclae. The rset can be a taotl mses  and you can still raed it wouthit a porbelm.   This is bcuseae the huamn mnid deos not raed ervey  lteter by istlef, but the word as a wlohe.  Amzanig  huh? yaeh and I awlyas tghuhot slpeling  was ipmorantt! if you can raed this psas it  on!!

  24. Current Predictive Modeling Activity • BioSignia – Mortality Assessment Technology (MAT) • ExamOne RiskIQ • CRL – SmartScore • Heritage Labs – Risk Score

  25. Challenges of Predictive Underwriting • Data may be predictive but also meet public acceptance thresholds and legal requirements • Anti-selection by agents • Reinsurance attitudes • Pricing – risk classification comparisons to traditional underwriting

  26. Benefits of Predictive Underwriting • Improved underwriting efficiency…and much, much more • Consumer, demographic, personal and financial data less expensive and more readily available than traditional underwriting tests • Smarter APS ordering • Fast – decisions in minutes or hours vs. weeks or months • Cheap – data is cheap, knowing how to use it may be another story • Premium growth – increased sales • Reduced process time increases placement ratios • Attract new producers • Target marketing – consumer data

  27. Conclusion • Evolution not revolution • Continue to make incremental process improvements within the parameters of your organization • Be cautious to avoid anti-selection pitfalls • Continue to stay tuned into advancements by reinsurers • RGA Re • Swiss Re

  28. The End! Additional reference: Predictive Modeling Comes to Life by Bary T. Ciardiello, David W. McLeroy

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