"Claims Leakage"—it almost sounds like a nasty toxic waste spill.
Yet, it is no joke and just as costly as a real spill. Claims Leakage (CL)
represents millions and millions of dollars lost in the insurance industry. And
that affects everyone.
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
Following is a discussion of the best technology solutions that can be
leveraged.
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 traditional reports. 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