Jim Kaiser is CEO of Casentric.

The use of data to gain deeper insight into claims performanceis growing rapidly. Technology and tighter process management isfueling this growth. Legacy system replacement provides offers theopportunity to collect more data as do other solutions such asmedical bill review, auto and property damage estimating or injuryand liability evaluations. Business intelligence systems designedto improve our ability to consolidate and analyze this data aregrowing in capability as well.

Before plunging headlong into the sea of data, it is useful totake stock in the historical challenges of data analysis in claimsto avoid having history repeat itself. In particular, it is vitalto understand what kind of sense can be made of the data wecreate—can it tell us “why.” To explore this question, this articlelays out data goals, the challenges that have impeded achievingthese goals, and comments on how these insights impact the nextgeneration of data analysis.

The Goals of Data Usage in Claims

In order to effectively consider and use data, it is critical toappreciate the goals of using it at all. As the Cheshire Catfamously pointed out, “If you don't know where you're going, anyroad will take you there.” This is important to mention becausediscussions about data these days can quickly gravitate tofunctionality—the path you can take—instead of outcomes—where youare going. By making this leap, it is easy to overlook thechallenges data has presented to reaching the destination.

The goal behind collecting data is to be able to understand whyand how a claim reached its ultimate resolution. This tells uswhether a claim varied from a desired outcome, why and by how much.By understanding variance, we can take action to reduce it and,thereby, improve the accuracy and timeliness of settlement.

Historical Challenges with ClaimsData

The temptation to design information capture that is overlybroad is great. This is driven by efforts to “force” considerationof important claims factors and, at the same time, account for allpossible fact sets. It is also necessitated, in some cases, byrules engines that require the data to produce outputs.

On the most basic level, forcing the collection of more datagenerally has limited effect. Adjusters and managers start tosuffer from data-gathering fatigue. The consequence is datacollection that is rushed and often inaccurate. Because of this,requiring more data collection is rarely effective at exposingvariance or its causes.

The more significant issue for analysts is the data's relevanceto understanding variance. Most often the data collected represents“attributes” or facts of a case. For example, liability attributescan include such items as the location where the loss occurred, thenumber of traffic lanes or speed limit. This information tells usthe “what” in a claim, but does not tell us “why” a case wasresolved for a particular amount.

It is, for example, far more important to understand whether aparty failed to yield right of way and what evidence supported thatdecision than it is to detail the nature of the loss site.Unfortunately, this “why” information is largely lost inunstructured claim notes, if it is ever captured at all.

On the other end of the data collection spectrum lies interfacesdesigned to run rules engines. While these solutions can be useful,the data types collected are rigid and tend to mask therelationship between the data entered and the output produced.Aside from thwarting learning (removing cause and effectrelationships has this effect) they can also require organizationsto focus excessively on managing accurate data inputs rather thansuccessful claim outcomes.

The issue presented by analysis of data that lacks a connectionbetween attributes of a claim and its outcome is that it leads tobroad reactions rather than surgical intervention. These reactions,in turn, generate unintended consequences—a whole new variety ofissues. For instance, if I examine average personal injurysettlement amounts in cases where the payment for property damagewas low, I might rush to a judgment that low impact cases areimproperly valued. This can cause a new set of procedures and,possibly, more data fields.

If, however, we know how low impact information is being used toadjust the value of the case we achieve both a well-reasoned caseand tremendous strategic insight. For instance, capturing the factthat low impact information was used, how much it affected value,and the reasons it impacted value by that much, our comprehensionof variance is immediately raised.

Obligatory data entry of marginally relevant data also createschallenges for understanding how a claim developed. When users are“forced” to fill out screens, they tend to put it off as long asthey can. This means loss of insight into when information wasreceived and it affected the course of the case at that point. Forinstance, it is very useful to know how and why a claim's valuechanged between the evaluation prepared to discussion resolutionand the amount for which a case was actually resolved.

Data Planning and Design

To serve the goals we outlined above, and others, claim datamust move beyond “what” to provide insight into the followingquestions:

• Why did we arrive at a value?
• What path did the claim take to arrive at resolution?
• Who made provided the justification?

By re-thinking the data we capture, we can simplify it. While itis not an easy task to catalog the conclusions an adjuster reaches,there are far fewer conclusions reached and reasons given for thoseconclusions than there are facts and attributes to a case.

There are only so many reasons that an individual can benegligent in a loss, but there are myriad circumstances in whichthat negligence can arise. In a world where we have less time toanalyze and act on data, having less data that provides deeperinsight allows more rapid and specific identification ofissues.