Many of the tasks involved with insurance claims are repetitive and important, but extremely mundane.
Checking boxes, sorting files and compiling spreadsheets for the sake of analysis
is low-valued, robotic work. In a digital
economy, putting live talent on tasks that
can be automated, such as data mining
and fact-checking, is not only expensive,
it’s opportunity lost.
Let’s look at the expanded use of
analytics in claims processing as it
applies to reducing cost, providing real-time insight to adjusters, improving the
customer experience and amplifying the
value of human resources in a digital
Defining predictive analytics
What is predictive analytics and how
does it impact decision-making?
In a world of big data, information
flowing into the claims decision tree
comes from a number of structured and
unstructured sources. Internally, histori-
cal records, case files, customer contracts
and other legacy information can help in
constructing predictive models. External
data may also contribute, including social
media, law enforcement records, actuar-
ial tables, financial data, geographic and
climatic inputs, customer mobile inputs
and other specialized information rele-
vant to specific claims and risk decisions.
Predictive analytics, for the sake of this
discussion, refers to the collection, sor-tation, compilation and presentation of
data in a way that makes it plainly understandable and useful in predicting specific insurance claims scenarios.
Unlike traditional actuarial analysis,
which relies heavily on assumptions and
usually lags in relevance, predictive an-
alytics provides a more robust, scientific
model of past and present information
for insight into real-time claims process-
To the adjuster challenged with the
task of quickly resolving insurance claims
to a customer’s satisfaction – while also
ensuring the lowest payout at maximum
recovery – predictive analytics can be a
veritable game-changer. Consider the applications:
• Fraud identification: Mining claims
for telltale signs of possible fraudulent activities.
• Litigation management: Flagging
for swift resolution cases displaying
the potential for escalated legal costs.
• Accurate reserving: Avoiding cost
inflation and step reserving on long-tail cases based on accurate payout
• Subrogation opportunities: Spotting
cases where liable parties might subrogate a claim to help recover claim
Are Robots in Your
How predictive analytics impact
cost & customer satisfaction
By Sean Allen