The ability to act on an account at First Notice of
Loss (FNOL) is essential for effective insurance
claims management. Identifying how an account
should be treated during the earliest stages of the
claims lifecycle can significantly reduce the
expense of managing an account.
Assigning the appropriate adjudicator to a claim as
early as possible can reduce cost and minimize
the need for deploying resources at later stages.
In addition, early detection of claims fraud
drastically reduces losses.
With predictive analytics, insurers can look at
retrospective claims data to define risk segments
based on various characteristics. Once segments
are identified, advanced modeling can be used to
create strategies that assign defined rules of
engagement to each segment.
Claims can be scored using a variety of metrics,
and placed into a corresponding segment. Using
the rules of engagement, examiners can assign
claims to teams best suited to managing them. In
the right hands, claim settlements take less time
and fewer mistakes are likely to be made during
the settlement process.
Predictive modeling techniques quickly determine
the risk level of a claim, how it should be treated
and by whom.
Many industry analysts agree that the most
important trend in insurance claims management
is the streamlining of operations
Predictive analytics can help insurers streamline
claims management processing. Insurers benefit
from applying predictive analytics to claims
management with:
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The common factor in all of these areas lies in an
examiner’s ability to conduct triage. If an examiner
can identify the appropriate way to treat a claim
based on its attributes, they can directly impact
operational efficiency.
Assigning the appropriate treatment to an account
as early as FNOL improves an insurers ability to
respond quickly with resources best suited to
handling that claim.
Despite these benefits, not all insurers can afford
to invest in the technical resources required to
conduct in-house analytics. Additional challenges
arise when insurers realize they must also
integrate disparate data sources from siloed
databases within their organization in order to
realize the benefits of analytics.
How can insurers overcome these hurdles to
deploy predictive analytics and realize claims
management operational improvements?
The emergence of insurance based predictive
analytics software and solutions enable insurers to
deploy sophisticated analytical solutions in record
time.
Traditional analytical approaches such as sourced
tip lines, customized watch lists, manual reviews
or adjudication, ad hoc querying and random
audits are still relevant. In addition, predictive
analytics software and solutions provide
customers access to:
 Advanced predictive modeling
 Anomaly detection
 Text analytics for unstructured data
 Automated rule–base
 Network/link analysis
 Claim scores
By supplementing traditional claims management
methods with advanced analytical services,
insurers realize benefits in the following areas:
Combined savings of as much as 30% in
expenses or losses incurred can be attributed to
the use of predictive analytics for claims
management.
The ability to quickly and accurately assign
personnel to specific claims at early stages
provides many cost savings. Faster time to
settlement means that less time is spent working
on individual claims.
Claims lifecycle efficiency also stems from the fact
that resources are put to better use. Supervisors
and experts can be assigned to cases that require
their skills, rather than spending time on poorly
assessed claims that do not need to be managed
by an experienced team.
Furthermore, the use of predictive analytics allows
insurers to profile and identify claims that will most
likely result in legal action on the part of the
customer. This early identification assists in the
appropriate assignment of cases to legal counsel,
and can greatly reduce the cost of legal expenses.
By identifying and ranking claims based on their
fraud risk, examiners can isolate cases that are
more likely to result in fraud. When conducted as
close to FNOL as possible, this can save insurers
weeks of processing and handling fees, as well as
adjustment and recovery costs by reducing the
number of paid fraud claims.
This process can work with both claimant fraud
and provider fraud. Customer claims can be
automatically flagged and investigated for validity
based on their score. This has the added benefit
of accelerating the process and settlement of
sincere claims by reducing the time spent tracking
and handling suspicious ones.
From a provider perspective, insurers can predict
the risk of fraud and segment providers based on
their associated risk. With this method, they can
identify and sever relationships with providers who
submit fraudulent claims for the benefit of
themselves or their clients. This can also serve as
deterrence, reducing future claims from providers
who were sent letters about suspected
overcharging or over servicing.
When a claims examiner can quickly assign claims
to adjudicators or investigators based on how
suited they are to handle a particular claim, there
are several benefits to be gained.
Examiners can classify prospective claims with
models built using retrospective data to determine
the complexity and risk involved with each.
Combined with an understanding of the
adjudicators and investigators available, they can
assign claims to the right teams as early as FNOL.
This means that the right individuals will handle a
claim from the beginning.
The result is speed to settlement and increased
ease in reaching a settlement in contrast to
circumstances where claims are reassigned.
Fraud detection through predictive analytics
requires fast turnaround to be truly effective. By
deploying business processes and supporting
systems to automatically review incoming claims
against risk profiles, a carrier can significantly
increase claims fraud detection rates.
The accuracy of settlements also increases since
adjudicators are able to easily identify claims that
are statistically abnormal. Based on unique items
or services claimed, and how they compare to
various standards, it is easier to identify cases of
inaccurate settlements.
Greater accuracy, combined with increased speed
to settlement and ease of processing improves
customer experience. This increases customer
retention and reduces churn as customers will be
less inclined to use an alternate provider or
insurance plan.
Angoss claims management analytics are offered
via Angoss KnowledgeSEEKER®, and
KnowledgeSTUDIO® products, as well as
ClaimGUARD™ , which is hosted and managed via
the Cloud. Each providing insurance claims and
risk management solutions for data profiling and
visualization, Decision Tree analysis, predictive
modeling, and scoring and strategy building.
As a global leader in predictive analytics, Angoss
helps businesses increase sales and profitability,
and reduce risk. Angoss helps businesses
discover valuable insight and intelligence from
their data while providing clear and detailed
recommendations on the best and most profitable
opportunities to pursue in order to improve risk,
marketing and sales performance.
Our suite of desktop, client-server and big data
analytics software products and Cloud solutions
make predictive analytics accessible and easy to
use for technical and business users. Many of the
world's leading organizations use Angoss software
products and solutions to grow revenue, increase
sales productivity and improve marketing
effectiveness while reducing risk and cost.
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