external data to the claims process, but
they typically use it in a reactive manner.
For instance, special investigation units
(SIUs) order point-in-time information
from police reports, medical reports, and
public records data. However, by using
external data reactively, carriers are leaving holes in the claims process—paying
potentially fraudulent claims, missing
subrogation opportunities, and allowing
severe claims to escalate in the hands of
inexperienced adjusters—while simple
claims languish on adjusters’ desks, driving up handling costs and negatively impacting customer service.
In contrast, a proactive claims handling
approach takes full advantage of multiple
data sources and analytics engines. From
first notice of loss (FNOL), carriers can
evaluate the claim and route it to the most
appropriate person or department: sending suspect claims to SIU, subrogation
opportunities to investigators, potentially
severe claims to experienced adjusters
and fast-tracking low- or no-touch claims
It doesn’t stop there. After all, the
claims process is dynamic, and the processes that support claims processing
need to be equally dynamic. As claims are
updated with new information, a proactive approach gives carriers a way to easily re-assess and re-route each claim, ensuring that each one is still in front of the
right person at the right time.
Integrating Data and Analytics to the Claims Process
By Deb Smallwood
One of the biggest challenges that insurers face is that the data needed to complete automated
transactions or to make a decision is typically spread across disparate systems. In many cases, the
internal data is incomplete and sometimes lacks both quality and accuracy. When these challenges
are coupled with the need to augment with multiple external data sources that all feed analytics,
the task at hand becomes very complex. The key is to not think broadly, but to start simply and
small. Insurers are not going to be able to fix everything all at once, but by understanding the overall
scope of the opportunity and employing a compass to give them direction, they can begin to develop
robust data capabilities and will then be able to unlock the power of advanced analytics.
For best results, insurers should:
E Develop a data strategy. First, insurance organizations need to understand what they have in
place. That task involves making an evaluation of their current data and an assessment of their
current analytics capabilities. Second, insurers need full knowledge about what is available in
the marketplace that can help them supplement those capabilities. Next, an overall data strategy
should be defined, one that aligns tightly with business objectives.
E Leverage external data. External data plays a critical role in enabling insurers to more effectively
and efficiently triage claims, reduce costs, and enhance customer service.
E Continually re-evaluate. Insurers must regularly evaluate their data and analytics capabilities,
especially as new techniques and data sources emerge, offering the potential to impact the
E Get buy-in from the business. Data and analytics should not be applied in a vacuum. They belong
to and impact the entire organization. They are important assets of the business as a whole.
The effective use of rich data and powerful analytics can improve decisions, solve business
problems, impact ratios, and provide guidance for the future. Insurers that are able to create a
strong partnership between business interests and IT have the greatest chance of success in
capitalizing on data and analytics for the good of the organization.
Advanced Analytics techniques
Advanced analytics techniques can help
a claims organization to develop more sophisticated, efficient ways to manage the
claims process. Here are some commonly
used advanced analytics techniques:
E Predictive models use algorithms to
identify patterns in data. Claims are
scored and then routed to the appropriate claims area, such as SIU or subrogation units.
E Business rules alert carriers when specific situations arise. These business
rules can be customized according to a
carrier’s particular needs.
E Identity matching monitors all claims
for entities—people or VINs—that are
on a watch list. Carriers are informed
when the entity appears on a claim.
E Data search enables carriers to search
structured and unstructured data for
words, phrases and names. This tool
is especially valuable, as unstructured
data is notoriously difficult to parse.
Pulling External Data
Carriers can reap the true benefits of
data and analytics by augmenting their
internal policy and claims data with external data sources.
“Insurers need to start leveraging the
external data that is available in the mar-
ketplace,” advises Deb Smallwood, found-
er of Strategy Meets Action, an insur-
ance-focused research and advisory firm.
Smallwood recommends looking at the
entire claims process to see how external
data can bolster an insurance company’s
internal data capabilities: “It helps to start
at the beginning of the process,” she adds.
“Upon submission of a claim, insurers can
pre-screen for fraud. They can also use
data prefill capabilities, for example, draw-
ing from accident reports to help populate
and validate information in the claim.”
Often, claimants are in a highly stressed
state when they report an accident, which
can adversely affect the accuracy and com-
pletion of the report. With insight from ex-
ternal data sources, customer service rep-
resentatives can verify accident and policy
information while offering more personal-
ized customer service. More critically, the
validated data results in greater accuracy of
the claim details so the carrier can process
the claim in the most efficient way.