ACE CLAIMS MANAGEMENT
ACE 4D:
POWER OF PREDICTIVE
ANALYTICS
Predictive data analytics is coming out of
the shadows to change the course of claims
management. A new approach, ACE 4D,
provides the tools and expertise to capture
and analyze both structured and unstructured
claims data. The former is what the industry
is used to – the traditional line-item views of
claims as they progress. The latter, comprises
the vital information that does not fit neatly
into the rows and columns of a traditional
spreadsheet or database, such as claim
adjuster notes.
Why is predictive analytics important
to claims management? Because it finds
relationships in data that achieve a more
complete picture of a claim, guiding
better decisions around its management.
This remarkable functionality is now at hand
to achieve unparalleled efficiencies and
cost-effectiveness when managing claims
for workers compensation, casualty bodily
injury, employment practices liability, and
other financial risks.
Predictive analytics alters this paradigm, offering the means to distill and assess detailed line-item claims information. In the hands of insurers and third party claims administrators, such analytical tools can, for instance, identify unrecognized potential claims severity and the relevant contributing factors. Having this information in hand, a claims professional can take deliberate actions to reduce or mitigate the financial losses generated by the claim.
To identify and prescribe specific actions that can positively affect claims outcomes, insurers need to undertake robust claims modeling. This has been challenging in the past because the tools available for such modeling had limitations. Unable to capture and assess unstructured claims data, traditional claims models issued predictions based exclusively on structured data. With more of the story now being told, the insurer’s ability to reduce the financial impact of a claim is more effective than ever before.
To make good on this value proposition requires the establishment of two processes – one ensuring that the right people receive the intelligence produced by the model in a timely manner, and another requiring specific deliberate actions to be taken based on the details. Predictive models just inform; people must intervene to seize upon this information to improve overall program performance.
The typical claim involves an enormous volume of disparate data that
accumulates as the claim progresses. Take the example of a workers
compensation claim. The data runs the gamut from the actual claim filing itself
to the action plan of the claims examiner; the medical file of the injured or ill
worker; his or her specific personal and economic demographics, job tenure and
medical history; the different medical services, medications and physical therapy
that were prescribed for the individual; the various transactional amounts paid to
date; and ongoing progress reports on the individual’s condition, to cite just a
few data sets. This vast volume of information compiles on a continuous basis
over time. Making sense of it all for decision-making purposes is extremely
challenging, given the sheer complexity of the data.
Information is everything in business. But, unless it is given to applicable
decision-makers on a timely basis for purposeful actions, information
becomes stale and of little utility. Even worse, it may direct bad decisions.
In the context of predictive analytics, the intelligence provided by a model
regarding a claim’s relative severity is only as useful as the manner in which
this information is received and acted upon. Obviously, the whole point of
predictive analytics is to apprise claim teams and company risk managers
of something before it occurs.
THE MODELING
OF CLAIMS
MAKING USE
OF THE MODEL
Sharper Focus
on Predictive Analytics
ACE 4D captures traditional structured data, as well as unstructured data in its advanced analytics models. Structured data includes the traditional information that is collected as part of the claim filing and investigation. Unstructured data focuses on the notes that are routinely written by claims adjusters while the claim remains open. By analyzing both data forms, claims adjusters and supervisors will achieve enhanced visibility into the driving factors of a particular claim. This knowledge can then be aggregated across the larger set of similar claims to develop programs based on more insightful and deliberate actions that target reducing claim costs.
Sharper Focus
on Action
For claims data to have value as actionable information, it must be accessible to prompt dialogue among those involved in the claims process. Although a model may capture reams of structured and unstructured data, these intricate data sets must be distilled into a comprehensible collection of information. To simplify client understanding, ACE 4D produces a model score illustrating the relative severity of a claim, a percentage chance of a claim breaching a financial threshold or retention level depending on the model and program. The tool then documents the top factors feeding into these scores.
Prior planning is critical. Simply adding responsibilities to the many tasks that claims teams must complete as part of their caseloads may not be effective as it creates the risk that proper mitigating procedures will not be performed to their conclusion. The solution for these deliberate actions to be meaningful and not disruptive is to shift the workload of their claims teams appropriately, enhancing their efficiency and effectiveness through their use of the model.
For predictive analytics to perform as intended there must be
consistency in execution. Execution requires the organization to
embrace predictive modeling. Actions based on the models must
be well defined and supported by technology and procedures
that capture and re-enforce the action.
LEVERAGING
THE INFORMATION
Sharper Focus
on Insight
Predictive analytics will identify claim characteristics that drive exposure. These characteristics coupled with claims handling experience create the opportunity to change the course of a claim. To test the efficacy of the actions implemented, a before-after impact assessment serves as a measurement tool. Otherwise, how else can program stakeholders be sure that the actions that were taken actually achieved the desired effects? Say certain interventions are proposed to reduce the duration of a particular claim. One way to test this hypothesis is to go back in time and evaluate the interventions against previous claim experience. In other words, how does the intervention group of claims compare to the claims that would have been intervened on in the past had the model been in place? An analogy to this past-present analysis is the insight that a pharmaceutical trial captures through the use of a placebo and an actual drug, but instead of the two approaches running at the same time, the placebo group is based on historical experience.
To do this, claims teams must be entrusted to capture the same specific, high-quality information – both structured and unstructured – on a consistent basis. Otherwise, the reliability of the projections will be undependable. Claims teams also must establish post-model actions to reduce claims duration and/or enhance claimant return-to-work goals, and then scrutinize the effectiveness of these actions on a routine basis. Merely running the model is not enough to foster positive change; actions and their impact must be consistently measured and monitored.
The capacity to mine, process, and analyze both structured and unstructured
data together enhances the predictability of data analytics. In other words,
the more clues the better the ability to deduce an outcome. But, there is risk in
not carefully weighing the value and import of each piece of information before
inputting it in the model. Overdependence on text, for instance, or undervaluing
such structured information as the type of injury or the claimant’s age, can result
in inferior deductions. The goal is to continuously sift the wheat from the chaff.
A BALANCING ACT
Sharper Focus
on Measurement
A major modeling pitfall is measurement as an
afterthought. Frequently this is caused by a rush
to implement the model, which results in a failure to record relevant data concerning the actions that were taken over time to reduce claim duration and severity. For modeling to be effective, actions must be translated into metrics and then monitored to ensure their consistent application. Prior to implementing the model, insurers need to establish clear processes and metrics as part of planning. Otherwise, they are flying blind, hoping their deliberate actions achieve the desired outcomes. Tools like predictive analytics are only as powerful as the precise processes surrounding their use.
From a claims standpoint, trying to predict an outcome without including unstructured data is like putting together a puzzle with missing pieces. This is not to imply that structured data has less value than its unstructured cousin. Information on a claimant’s age, injury type, and occupation are critical elements in predicting the outcome of the claim. But, a far richer story can be told when mining and adding relevant unstructured data to the statistical analyses. Such data, for instance, may include information indicating that a workers compensation claimant recently put on a lot of weight or is under severely stressful conditions at home or at work. Maybe the claimant a short time ago started taking a prescription medication that is unrelated to the claim, but has claim-related ramifications. The medication could influence the treatment plan, return-to-work options, and claim duration. Perhaps the claimant recently
separated or divorced a spouse. This may affect the physical and emotional support he or she was to receive at home, with resulting claims implications. Not all of the above examples will be present on enough claims to include in a predictive model, but the goal is to identify as many as possible and test each as to they relate to claim outcomes.
Unstructured data has vital import to the management of a claim. Since this form of information is not static and
The industry has long relied on structured data to make business decisions.
But, unstructured data like claim adjuster notes can be an equally important
source of claims intelligence. The difficulty in the past has been the capture
and organization of this fast-growing source of information. With ACE 4D,
this is no longer the case.
WHY UNSTRUCTURED
DATA IS VITAL
Sharper Focus
on Data
Often buried within a claim adjuster’s notes are nuggets of information that can guide better treatment of the claimant or suggest actions that might lower associated claim costs. Adjusters routinely compile these notes from the initial investigation of the claim through subsequent medical reports, legal notifications, and conversations with the employer and claimant. This unstructured data, for example, may indicate that a claimant continually comments about a high level of pain. With ACE 4D, claims teams can discern how many times the word “pain” appears in the notes. Algorithms – a set of problem-solving rules or instructions – inserted in the model can determine the relationship between the number of times the word appears and the likely severity of the claim. Similarly, the notes may disclose a claimant’s diabetic condition (or other health-related issue), unknown at the time of the claim filing but voluntarily offered up by the claimant in conversation with the adjuster. These insights are vital to evolving management strategies and improving a claim’s outcome.
The reason is – like people – predictive models cannot know everything. There will always be nuances, subtle shifts in direction, or data that has not been captured in the model requiring careful consideration and judgment. People must take the science of predictive data analytics and apply their intellect and imagination to make more informed decisions.
Extracting the right information from a model also requires inputting the right data. The same can be said for getting the right information at the right time into the hands of the right people. Predictive models must produce meaningful analyses that are easily comprehensible and accessible to all relevant decision-makers. Finally, the information provided should be as straightforward as the actual analytics is complex. A report or screen with dozens of complicated variables will not be acted on. An approach that filters out the noise and fine-tunes the key variables that have, or support, a causal relationship with a claim’s outcome has a better chance of being read, digested, and acted upon.
While the science of data analytics continues to improve, predictive modeling
is not a replacement for experience. Seasoned claims professionals and risk
managers will always be relied upon to evaluate the mathematical conclusions
produced by the models, and base their actions on this guidance and their
seasoned knowledge.
THE BOTTOM LINE
Sharper Focus
on ACE 4D
So why consider products embedded with ACE 4D as your trusted partner in advanced claims analytics? We have made substantial investments in this area that continue. We have put together a team of nearly 40 professionals who are dedicated to modeling, and continue to investigate and deploy the necessary tools to translate data into information. And we see great value in our tools helping our customers reduce costs and increase efficiencies.
CONTACT US Steve Laudermilch SVP Claims 215-640-4917 Keith Higdon VP Claims 312-669-7521 www.acegroup.com
Insurance provided by insurance companies within ACE Group. Product information is a summary only. All products may not be available in all jurisdictions. The services described herein are conducted on behalf of the insurer and are not intended as a direct benefit or service to the insured or client; they may not be available on all ACE Group claims, and are not loss control services intended to prevent claims, losses, injuries or accidents from occurring. ACE makes no guarantee that the services described herein will result in reduced or mitigated cost on any claim. ACE Group is one of the world’s largest multiline property and casualty insurers. Headed by ACE Limited (NYSE: ACE), a component of the S&P 500 stock index, ACE Group has operations in more than 50 countries and serves a diverse group of clients.
©2015. All rights reserved. 617546 4/2015
