EXE CU TIVE E D G E
Now we’re at the
beginning of a long
rally race of analytic
improvements – from
newer, better data to
smarter, faster algorithms.
Enhancements will include
broader, more scalable
platforms and will
access unique sensors –
spectra, spatial, temporal
– and micro/macro
levels of structured and
unstructured data.
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unique sensors – spectra, spatial, temporal – and
micro/macro levels of structured and unstructured data.
All that will help generate insights into individualized,
massively personalized and localized information while
getting even more power out of grouped predictive
parameters.
For example, if you have operated a car or other
moving object, you undoubtedly have been assessed
for your risk of loss as an operator of that vehicle and,
in some manner, likely have been insured. In the past,
that insurance-based risk assessment has blended wide bands of information on a few historically
available generic characteristics to achieve a generalpurpose estimate of prospective loss risk estimates.
In the future, that historical benchmark will be
segmented into ever-more granular and accurate
assessments. Those will then again be reinvented,
recombined and refined to enhance the data-driven
process, culminating in an adaptive analytic that adjusts expectations to the level of risk in each operating scenario encountered or intuited.
In the world of big data and bigger analytics, insurers will come to view vehicles as instrumented
platforms and operators as real-time learners whose
risk may change over time. Operators may drive safer
and make smarter decisions about moving between
locations, or they may permit distractions into their
cockpit (such as texting, talking, smoking, eating and
so on). How you drive, when you drive, where you
drive and how much you drive are all becoming part
of the context in your individual risk profile.
Some businesses apply detailed telemetry, routing algorithms, real-time weather and traffic alerts
and driver/crew pairing models to manage more
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