Transform Your Claims Litigation Management Program
with Structured Data
Harness the Power of Your Data and Change How You Manage Litigated Claims
Litigation in the property & casualty claims industry is both a source of frustration and an opportunity for claims and litigation management leaders. Approximately half of all claims organizations continue to report escalating per-file costs in their claims litigation management program. A full 80% of claims executives say that a majority of their litigated claims settle later in the process “than is necessary.”1
For many claims organizations, attorneys’ fees and expenses exceed the unallocated expenses required to maintain the claims department. And despite the significance of litigation costs, they pale in comparison to what is spent on the indemnity side of the fence — the amount paid to resolve the cases themselves. It is no wonder that more than seven out of 10 senior claims executives report having had a conversation with their CEO about litigation management effectiveness in the last 12 months. 1
Why is this the case? How can costs continue to rise, and cases continue to linger, when there is an entire $500+ million industry focused exclusively on legal invoice review and legal e-billing software?
In our view, as we explain below, it is because there remains too many unstructured data sources in the litigation management process. Claims executives have repeatedly demonstrated that they can strengthen any process, eliminate any inefficiency, and improve any outcome with the right data elements.
Without the right data, however, these improvements are challenging at best. Which attorney is the right one for this case? In this venue? Which attorney is most likely to win at dispositive motions? Which ones get the plaintiff deposed most quickly? Which are best at pursuing early resolution strategies? Which get the best outcomes? Which do best in front of this judge? With this plaintiff counsel?
These are the data points that claims executives need to affect actual change. Without these answers, and with only invoice-centric data, claims executives must focus on which attorneys are the least expensive (not which ones get the best outcomes). Perhaps this is why claims leaders today rate the “helpfulness” of their current litigation metrics at a lukewarm 55 out of 100. 1
Unstructured Data vs. Structured Data
Let’s talk quickly about data. Simply put, unstructured data is anything (files, text data, documents, communications, imaging, media, etc.) that isn’t stored in a structured database format. Unstructured data is the data you collect that is not captured and managed in a transactional system.
All industries suffer from some level of unstructured data collection, and your claims litigation management program is no different. Every report, email, and communication from counsel — all the information contained in those formats — is unstructured.
In the claims litigation world, examples of unstructured data include:
- Email conversations where important dates, decisions, results, and other information is buried in the communication test.
- Documents containing critical claims information and are attached to emails or other forms of communication where, like email, the data is embedded in the document and not easily accessed.
- Invoices containing important information related to the case and all the legal professionals providing services, explained line by line.
- And many would argue, individual file strategies. File strategy is essentially a series of agreed-upon next steps and activities. What are they? When are they due? Have they been completed? In today’s current environment, the answers to these questions remain unstructured.
Structured data, in comparison, is data that you can capture in specific fields in a database. Field-centric data, particularly the ability to see how each data element has changed over time, can be used in reports, correlated with one another, and analyzed.
Said another way, everything of importance that you wish to measure, report on, correlate or analyze needs to reside somewhere specific. Data critical to measure your claims litigation management performance needs a home.
As you can see in our example below, claims organizations currently receive a wealth of valuable data points and informational elements during the course of their litigation. The problem is, the vast majority of the most important data points are received in an unstructured format. They cannot be correlated, measured or analyzed.
How Structured Data Can Be Transformative
Let’s talk about a real-world example in another industry. In 1999, a former Oracle executive, Marc Beninoff, and a collection of partners launched Salesforce.com. This SaaS-based customer relationship management service intended to structure the data around new customer acquisition, customer service, marketing automation, analytics and other services.
A Real World, Claims Litigation Example
Reflect on the last time that you looked at a closed litigation file. Think briefly about the wealth of information counsel provided to you about the case. (They provide these data points, and more, on just about every case that you assign to them to defend):
- What was the case venue?
- Who was the judge?
- Who was the plaintiff’s attorney?
- What was the last demand? What were past demands?
- What was our last offer? What were previous offers?
- Was there a dispositive motion in this case? What was the outcome?
- When were the key depositions? How many times did those dates change? Why?
- Did mediation occur? Who was the mediator? What was the outcome?
- Which experts were used? Were they helpful? How helpful?
- What’s was the predicted exposure on the file? How accurate was that prediction?
- What was the initial expense budget? What was ultimately spent on the file
- What is the agreed-upon strategy of activities on the file? Were those activities executed? How quickly?
You might think that meaningful analytical work would be a breeze with all this data in the file. But, of course, it isn’t because the information is not actionable in its unstructured form. It sits right where it came in, in a letter or an email.
True, the claims organization could ask its claims professionals to pluck out the key data points and re-key them into a claims system in some way, but this is a terribly inefficient and expensive activity. Claims organizations want their professionals to be making strategic decisions about resolving the file, focusing on coverage, liability, causation, and damages, not re-keying data.
Instead, picture an environment in which counsel provided all of the data elements they are currently providing but did so in a structured way. Instead of writing a long letter, they complete a series of templated forms, and then add their softer analysis and comments elsewhere. No extra work; just a different way of sharing the information.
This dramatically changes how claims executives can see the big picture and how claims professionals can then intervene where appropriate, guiding existing cases toward best practices to get to faster case closures and improved outcomes.
Big Picture Data Example – Cycle Time
How do you suppose a company’s leadership team would respond to saving over $1.2 million because they adopted a structured data system that allowed faster case closure and improved indemnity outcomes? You would expect the response to be positive.
According to CaseGlide’s Director of Account Management, Andrea Stachnik, “We find that once we implement a new claims organization on the platform, they’re able to analyze their data and close around 10% of their current cases immediately due to more closely understanding the specific aspects of the cases.”
That’s what structured data can do. Much like how Salesforce.com revolutionized the way companies conduct their sales efforts, structured data in a claims litigation setting can provide the data intelligence you need to improve how you drive success in your organization.
A Future for Claims Litigation Management
Though it seemed daunting to some at the time, organizations successfully met the challenge of getting their sales leaders and personnel to adopt Salesforce.com in exchange for better results, actionable data and analytics, and increased efficiency. Claims litigation management leaders can now do the same.
Think of the various data elements we’ve described above: venue, plaintiff counsel, judge, exposure predictions, demands and offers, dispositive motions, file defensibility, file severity, scheduled depositions, expert utilization, use of mediation, expense budgets, and amounts spent to date — the list goes on and on.
The range of use-case scenarios for deploying these data points is extensive. This data can identify developing trends over time, correlated, and analyzed at macro and micro levels. It can also help identify the best firms for certain types of cases, or the best attorney.
This data can also lower cycle time and improve outcomes proactively by affecting earlier resolution strategies. Here are some simple examples:
- Show me all cases where the offer and demand are within X% of one another, or within $Y of one another.
- Show me all cases where the difference between demand and offer is less than the remaining expense budget.
- Show me all cases with a high-severity rating and poor defensibility rating.
- Show me all cases where counsel is behind in executing our agreed-to strategy.
- Show me all upcoming scheduled plaintiff depositions (maybe I’d like to make an offer after that event).
These are just five of many, many scenarios on an extensive list. Every litigation executive can think immediately of five reports they’d like to see but can’t currently retrieve.
Think about the efficiency gains for every claims professional who needs to be able to easily access information about what has happened in a case, what is overdue, and what is scheduled to occur. Every data point is in its place and logically organized. No more searching through the last reports, emails, and correspondence.
By asking your attorneys to input information into a litigation management software platform, you can better organize the data, improve efficiency, and prepare for the future.
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