This is the first part in a series by Vince Venturella on the future of claims litigation technology. Part one focuses on predictive analytics. In subsequent posts, Vince will cover artificial intelligence (AI) technologies as they apply to claims and tools that support “red flagging” of potential problems with claims, such as fraud or challenging precedents in the case law.
Technology has been changing the claims landscape for the past three decades or more. From the adoption of laptop computers to email and more advanced claims systems, technology has been changing the way claims groups operate, run their business, and achieve productivity while delivering outcomes for their policyholders.
Now, however, we sit upon the precipice of real change for the first time. The previous changes were technological improvements that gave us convenience, allowing users to complete the same workflows they’d had since paper and pencil but more quickly, efficiently, and accurately. The next generation of technology, such as AI, stands to completely change the way claims litigation work is done. It has the potential to reinvent workflows, disrupt longstanding assumptions within the litigated claims space, and empower humans while eliminating work that doesn’t add value.
What Is Predictive Analytics?
In this first installment of this series, I am focusing on predictive analytics as used in the litigated claims space. Predictive analytics is a type of data analysis that uses historical and current data to predict future events. These types of tools often leverage machine learning, a type of artificial intelligence, to help spot risks and opportunities for companies. In the claims litigation environment, specifically, predictive analytics can provide information to help with the selection of law firms, the setting of budgets, identifying the expected total time to resolve a case, when to settle, and everything in between.
Seasoned claims professionals performing these tasks have a deep well of experience they can draw upon, making inferences based on all the past cases they've handled. Newer claims team members, however, lack this experience and therefore find it much harder to make these decisions with the same clarity as their experienced peers. That’s where an AI tool can help.
Predictive analytics effectively bridge that knowledge gap and allow junior associates, or those coming to new lines of business or new geographic areas, to benefit from the collective knowledge of everyone who has come before. By analyzing relevant historical information, comparing it to recent data, and identifying the likelihood of various events and outcomes, predictive analytics allow even relatively inexperienced claims professionals to make well-informed decisions.
More senior associates can benefit, as well. Beyond informing routine decisions, predictive analytics can also help spot early potential challenges and shine a light on cases that may be more complex or costly than is immediately obvious. This is an area I’ll explore further in part three of this blog series.
The Trust Hurdle
As with most technology that people aren’t yet used to, the challenge is: will people trust the technology? Getting AI to make these predictions isn’t difficult with current tools. We at Wolters Kluwer’s ELM Solutions have built models that can access data from our ELM platforms and claims systems to make highly accurate predictions about the individual elements of the case, as well as things like what outside counsel firms would be most likely to drive a positive outcome.
When this information aligns with the claims professional’s expectations, there is no issue. Their knowledge is confirmed, and they have more confidence in their decision and experience. However, when the result runs counter to their expectations, what do they trust? Often, they trust themselves, which leads to a distrust of the new technological tool.
This is the arc of all technology, however. At first, we don’t trust the technology because we are accustomed to relying on our own experience and ways of working. It got us this far, after all. For example, when I got my first cell phone, I didn’t program every number I knew into the address book. I knew I could remember them and had them written down somewhere already in case I did forget.
Now, I have no idea what anyone’s phone number actually is, even people I’m close to. Over time, as we integrate the technology into our practices and it consistently provides accurate and useful information, we come to trust it. We are still relatively early in the adoption process for predictive analytics, but more and more claims teams are using it over time. Soon, we’ll have more confidence in this technology, and the job of claims litigation professionals will be easier in many ways.
Learn more about how one class of solutions, AI tools, can help insurance professionals in our whitepaper, Artificial Intelligence in Claims Litigation: Meeting Challenges and Predicting the Future.