Reducing the amount of leakage has become one of the most important areas
of strategic concern to insurers, third-party administrators (TPAs), self-administered
organizations, and, of course, to the ultimate client: the policyholders. How
to reduce the negative impact of CL through the effective use of technology
is the focus of this article.
Definitions
As with any topic involving insurance and technology, proper definitions
are in order, especially if an acronym is lurking nearby. Claims Leakage is
simply defined as lost dollars through claims management inefficiencies that
ultimately result from failures in existing processes (manual and automated).
Or, as one unnamed claims executive said "the difference between what you
did spend and what you should have spent on a claim."
Those differences can be explained by any or all of the following.
Process Issues:
- Inefficient claim processing
- Improper/errant payments (which can result in fines and penalties in
a workers compensation issue)
Human Issues:
- Poor decision making (missing the opportunity of settling a bad claim
early in its life cycle)
- Poor customer service (unreturned phone calls, longer claim processing
time)
- Fraud
Identification of CL can be accomplished through an audit of closed claim
files. Analyzing the results of the closed claim files (i.e., how they were
settled or closed) by comparing them against a number of leakage factors to
determine the accuracy of the closure will show how bad the leakage is. For
example, how consistent were the settlement decisions made when compared across
the claim organization? Were proper reserving and settlement guides used? Was
investigation sufficient to reduce the likelihood of fraud? Were subrogation/third-party
recovery attempts made consistently and effectively across this sample of claim
files?
Once this review has been conducted, and an idea of the extent of the leakage
has been determined, what is the next step? Of course, hiring good claims people
utilizing efficient processes is a logical and time-tested solution for the
problem of claim leakage. But this article's focus is on the tools that can
be brought to bear.
Technology Solutions
The best technology solutions that can be leveraged are discussed below.
State of the Art Risk/Claims Administration System
From the perspective of the claims professional, a well-designed claims system
is essential in both preventing and discovering CL. Simple human error can be
prevented by the claim system's self-auditing features. For example, it would
be impossible to process a payment in excess of reserves left available due
to the built-in edit of most updated systems.
Unfortunately, many TPAs and insurers still have older, legacy-based systems
with little interaction with other systems (i.e., nurse case management, medical
management, underwriting, loss analysis/safety software, etc.). That makes it
more difficult to spot trends and prevent foolish errors.
A more advanced claim system, however, will provide:
- A more flexible and extensible claim data model;
- Simplified claim function through consolidation of claims data, integrated
systems, and reporting platform;
- Improved data-based decision monitoring;
- Improved monitoring of claims professionals (adjusters, examiners, payment
processors); and
- Improved self-auditing function to prevent regulatory expenses (fines).
This is both good for the claims professional and the ultimate end user:
the client. Some clients who are large enough to self-administer their claims
may take advantage of some of the most advanced claims administration systems,
such as Aon eSolutions' IVOS, Marsh STARS' Enterprise or Professional Edition,
and CSC's RiskMaster. Each of these systems, as well as a host of others, provides
these single, interfacing claims system solutions.
Reporting Analysis
Another feature of these newer claim systems is a reporting capability to
analyze the vast amounts of claims, policy, and ancillary data within the database.
It is the reporting tool that can be used to develop metrics for claim personnel
performance as well as the analysis of claims processing. Many insurers and
larger TPAs have developed client oriented claims systems that serve as the
window of the claims program to the client. Some of the more sophisticated programs
are Travelers' eCarma system, Chartis's IntelliRisk, and Sedgwick CMS's ViaOne.
These automated tools will equip the risk manager with the ability to analyze
the claims data in such a fashion to identify potential and actual CL. For example,
the following template reports are generally available through these systems
and can identify some problem areas:
- Average settlement cost: Looking at
this trend over time can give evidence on claim values. If it is trending
upward, more investigation is necessary to determine if there is CL involved.
- Total caseload/adjuster: By itself,
it is not an indicator of CL. But if the caseloads start increasing, one
can ask whether all of the investigation and analysis is being done by an
overworked claim examiner.
- Number of reserve changes per claim:
This is another indicator of potential claim leakage. Many reserve changes
in a relatively short time frame may indicate stair-step reserving which
is generally an inefficient method. It can mean potential CL.
- Average time of first contact of claimant:
Commonly known as a lag time indicator, this statistic is a leading indicator
of CL. The longer the average first time of contact, the greater the chance
of the claim increasing in value over what it should have been.
- Claim turnover ratio: This is simply
the number of new claims in during a month divided by the number of claims
closed (either by closure, settlement, or denial). If the numerator greatly
exceeds the denominator, or if the trend of the ratio continues to rise
over time, it is a sign that there is a workload problem. That usually translates
to CL.
- Subrogation recovery trend: This statistic
simply shows the success rate of recoveries against outgoing claim payments.
A shrinking trend of recovery can definitely indicate CL.
These are generally available reports available through most claim information
systems. However, there is an even better and surer method of determining the
origins and extent of CL: predictive analytics.
Predictive Analytics Reporting Capability
Predictive analytics (PA) or modeling is the discipline that analyzes current
and historical facts to make predictions about future events. It is a much deeper
dive than the traditional reports that were discussed in the previous section.
And PA involves more than just the claims data as it also seeks information
from as many divergent sources affecting claims as possible.
As to claims leakage, PA will focus on areas such as fraud detection: either
missed through inadequate investigation or identification of actual fraud (i.e.,
revealing a coterie of law firms, chiropractors, autobody shops, and professional
claimants working together).
Many insurers and system vendors are providing this capability to claim professionals
and risk management professionals. Some vendors, such as Marsh STARS and Aon
eSolutions, are using Predictive Analytics techniques to identify CL. Deloitte
has developed a practice utilizing PA and modeling using not just claims data
but other ancillary data to identify problem areas for CL.
Conclusion
Controlling claim leakage is an important method in reducing a company's
ultimate cost of risk. And for the claim organization, it is a way to differentiate
itself from other providers as a superior organization. Proper usage of claim
systems, basic reporting analysis, and advanced tools such as predictive analytics
can be valuable allies in reducing this cost.
*The
Albert Risk
Management Consultants claims management team (Stuart Cowan, Lisa Hartman,
William Quinn, Jr., and David A. Tweedy) contributes articles
on claims topics. You can reach David Tweedy at