When it comes to fraud investigation, historically the process has
been anything but quick.
Insurers are obsessed with cycle time. They count the days it
takes to make a claim adjudication decision, the minutes it takes to complete
the loss intake process and the seconds it takes to process a transaction.
Especially in high-volume environments, time is money.
In the wisdom of insurance claims executives, faster claim
payments generally equate to better customer satisfaction and loyalty. Anything
that slows the process is burdensome and costly.
Of course, accuracy is important too. Just printing checks for
anyone who calls in to the claim center would be quick, but not terribly
accurate. So the key to all great claims organizations is to strike the right
balance between speed and accuracy.
When it comes to fraud investigation, historically the process has
been anything but quick. Many organizations still rely on a manual process
where adjusters identify red flags and notify the Special Investigation Unit
(SIU) by email or even a paper form. There is some sort of triage process, and
then an assignment to an investigator within the company or from a vendor
partner. And then of course, it takes time to do the actual investigation:
Schedule and reschedule appointments for interviews, track down witnesses,
review evidence and document the findings.
Many organizations are implementing analytics to help streamline
the fraud detection process. The Coalition Against Insurance Fraud reports that
more than 80% of US insurers are using some kind of fraud detection technology
today, and nearly one-third expect increases in technology budgets with
predictive models being a top area of investment.
How fast is too fast?
Analytical fraud detection models provide insurers with a great
advantage. They are ever-vigilant, always scanning the data and not letting
anything fall through the cracks. They can quickly identify risk flags in new
information as it is added to a claim file. Models can look across large
numbers of claims to see patterns and identify relationships that would not be
detectable by a human.
But one of the greatest benefits is speed. Optimized models can
scan an entire book of business very quickly. Inevitably, when implementing
this technology, the subject of "real-time" processing will come up.
While speed is a key benefit of analytics, insurers must be mindful of how the
analytics will be deployed. Insurers should define what is meant by
"real-time." For many technology providers, real-time transaction
processing involves sub-second response times most often used in credit card
processing. While it is possible to design a similar solution for the insurance
environment, it is expensive and often unnecessary. When evaluating processing
needs, insurers should ask themselves a key question: Even if it is possible to
get a "real-time" response, are we prepared to consume the results in
real time? If not, other less expensive options might be preferable.
When considering options for implementation of an analytical fraud
detection platform, there are a few options for processing. Real-time
processing provides instantaneous response times, often measured in
milliseconds. It is generally appropriate for high-volume transactions with a
limited number of highly consistent variables when an immediate decision is
required.
Near-real-time processing provides a short delay in response time,
often measured in seconds or minutes. It can often be done by running intra-day
batch cycles.
Batch processing is generally used when processing time takes
minutes, hours, or even days. It can accommodate very large or very complex
data and computations at a reasonable cost.
In many implementations, a combination of these approaches can be
used. For example, fraud scoring on long-tail workers' compensation claims
could be run on a weekly batch basis while short-tail auto damage claims could
use an intra-day batch process that runs every 15 minutes.
Another approach is to use batch processing for very complex
calculations like advanced network building and text analytics while using
faster processing engines for transactional claim scoring.
Where speed really matters
In the claims environment, most organizations can get by with
batch processing for fraud detection scoring. However, there are several places
where real-time detection can pay big dividends. Here are three
recommendations:
1. Point of sale processing. During the application and
underwriting process, especially with more insurers expanding their direct
Internet channel, real-time interdiction is critical. If a high-risk
application is flagged in real time, it can be routed for more thorough
validation and underwriting before unnecessary risk is taken.
2. First Notice of Loss (FNOL) processing. Insurers are always
looking to streamline their loss intake process. For claims accuracy and
customer satisfaction purposes, it is best to get the correct resources assigned
to the claim as fast as possible. During intake, it would be advantageous to
run fraud risk scoring models, which could direct the intake processor to ask
for additional information or automatically route the file to a more
experienced adjuster or SIU contact.
3. Claims workflow processing. As information is added to the file
throughout the life cycle of a claim, new decisions need to be made. Supporting
resources for medical case management, SIU, subrogation, etc. need to be
assigned. This is traditionally handled by libraries of business rules. But
more advanced analytical approaches can predict the need for these types of
resources. Implementing real-time analytics that work in conjunction with claim
system workflow processing engines can yield the best results.
The future is faster
Technology is ever-changing, and the current focus on big data
analytics is driving innovation, especially in the ability to process large and
complex data sets very quickly. High performance analytics takes advantage of
improvements in grid computing, in-database and in-memory processing, and
low-cost commodity hardware. In the future, insurers may not have to worry as
much about the tradeoffs between speed and cost. But for now, it pays to make
an informed decision.
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