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