high tech processing: from application to policy issue
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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. - PowerPoint PPT PresentationTRANSCRIPT
High Tech Processing: From Application to Policy
Issue
Presented by Keith HoeffnerFebruary 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
Don’t Be Trapped In A Paradigm
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 WorkflowReduces 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
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 are made possible through Web Services – a method of communication between two electronic devices over the web
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
Look very closely at the next animated slide…
Which way was the woman whirling?
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
Predictive Underwriting – Paul Hately, Swiss Re
Maybe I’m just not smart enough to figure all this out. Are you?
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!!
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