abling technologies. The scoring solution
is a combination of processes that col-
lects both the internal and external data,
transforms the data into the appropriate
format, calculates the score, generates the
reason codes explaining 85 to 90 percent
of the score in “English,” and feeds the
business rules that ultimately drive the
appropriate business actions. The scoring
operation is essentially the technical man-
ifestation of the claims predictive models,
often referred to in the industry as the
“scoring engine” or “scorecard.”
Leveraging this added analytics intel-
ligence requires the modification of busi-
ness processes, the re-routing of lost time
and medical claims, claims staff screen up-
dates/enhancements, the development of
new analytics based KPIs, and other mea-
sures. The modifications may translate to
enhancing existing infrastructure, all the
way to integrating new business rules en-
gines and/or workflow management tools.
In the end, the most successful companies
seamlessly integrate the analytics results
in a non-intrusive way to the end users,
and they do so in real time. If you find you
are compounding the workload of your
professionals, then your technology inte-
gration may need to be revised.
The Journey Continues
In the end, a claims predictive model
deployment is more than just a bunch of
actuaries, statisticians and PhD’s having
fun with numbers. The end-to-end analytics journey includes:
1. Model deployment strategy.
2. Claims predictive model development
(that is, cool mathematical equations).
3. Scoring engine development and technical integration.
4. Business and operational implementation.
5. Organizational change management.
6. Performance management and loop-back improvement.
If the organization can clearly articu-
late its strategy, develop a well-defined
implementation plan, and foster a strong
sense of ownership from the end users as
discussed in this article, we believe the
organization can be well on its way to re-
alizing the financial benefits from using
claims predictive models.
1 See the July 23, 2012 National Underwriter Company article titled Enhance Workers’ Comp Predictive Modeling with Injury Groupings.
2 Predictive models are run at FNOL, three-point
contact investigation, and monthly for approximately 12 months.
Kevin M. Bingham is a principal at
Deloitte Consulting LLP in Hartford, CT
and leader of the Claims Predictive
Modeling and Medical Professional
Brian Bingham is a Senior Manager at
Deloitte Consulting LLP in Boston, MA,
and leader of the Advanced Analytics
Richard Messick is a Specialist Leader
at Deloitte Consulting LLP in Hartford, Ct.,
specializing in claims and the business
implementation of predictive models.
Amel Arhab is a Manager at Deloitte
Consulting LLP in Boston, MA, and lead
modeler for the Claims Predictive Modeling practice.
Geetanjali Chakraborty is a Senior Consultant at Deloitte Consulting LLP in Boston, MA, and a member of our Advanced
Analytics & Modeling practice.
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