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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 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 • 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
Life Insurance Application Process COMPLICATED
Wide Open Possibilities • Straight through processing • Plus data mining • Real-time transactions • Workflow improvements • Predictive modeling
Straight Through Processing End-to-End Life Insurance Application Workflow Reduces Cycle Time by 14+ Days
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
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
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?
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
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
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
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
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
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?
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)
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
Maybe I’m just not smart enough to figure all this out. Are you?
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Current Predictive Modeling Activity • BioSignia – Mortality Assessment Technology (MAT) • ExamOne RiskIQ • CRL – SmartScore • Heritage Labs – Risk Score
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
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
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
The End! Additional reference: Predictive Modeling Comes to Life by Bary T. Ciardiello, David W. McLeroy