This is the third post in a blog series on how AI can help insurance claims and corporate legal professionals improve efficiencies and enhance operations. In this post, we’ll address how AI can help claims managers and corporate legal professionals to develop accurate budgets, estimate settlement outcomes, select the best possible counsel for every matter, and make better overall decisions through the use of predictive analytics. And be sure to read the first two posts in the series, AI is Real—And It’s Helping Corporate Legal and Insurance Claims Professionals and Now’s a Great Time to Use AI to Improve Your Invoice Processing.

Corporate legal teams are being asked to do many things, including predict the future.

Wait. What?

That statement is true…at least in a way. In addition to discovering efficiencies wherever possible and finding new ways to effectively control costs while keeping customers happy, legal professionals need to be able to make decisions that will impact their organizations in the future. For instance, they need to be able to accurately estimate final settlement amounts and outcomes and select the best possible legal counsel to represent their organizations.

Raw data and educated guesses

The problem is that all of these decisions are generally made based on limited information or, worse, gut feelings. Sure, corporate legal departments (CLDs) have a ton of raw data, but there’s a good chance that most of it hasn’t been analyzed and placed into a format that is conducive to making informed decisions. As a result, attorneys are often forced into educated guesses.

But educated guesses no longer cut it in today’s environment, where CLDs are expected to provide business value just as much as legal expertise – and are not just unrestricted cost centers. They need a way to take the information that exists in invoices, case outcomes, hours billed, and elsewhere, and use it to make actionable, informed decisions.

No more guesswork

This is where AI, predictive analytics, and machine learning come into play. Predictive analytics is the practice of using historical and current data to make informed decisions about future projects. AI and machine learning technologies can evaluate that data and automatically provide users with recommendations.

These technologies are changing the game for CLDs and their outside counsels. Through these technologies, teams can now make careful and considered law firm selections based on actual historical data. They can also develop better and more accurate predictions regarding budgets and estimated settlement amounts.

Let’s take a look at how this works in practice.

Selecting the right counsel

Selecting the right outside counsel for a particular case can be tricky. Often, legal teams and claims managers try to circumvent the complexity of the process by simply turning to the most recent law firm partner they’ve used, a phenomenon known as “recency bias.” But these decisions aren’t based on fact, and the chances of selecting the wrong partner are high. Who can afford to take those chances?

AI can offer a much better indication of whether or not a firm is the right fit for a particular matter. AI collects and analyzes information from past cases and performance and applies the metrics to the present situation. Based on this data, the AI can make a recommendation as to which firm would likely be the best fit. No gut feelings necessary.

Developing accurate estimates

Once a partner is selected, the questions begin. How much should we settle for? Should we settle and when? What’s our budget? What are the range of potential outcomes?

Litigation defense attorneys and claims managers have a great deal of experience in these matters, and usually have a solid idea of how to answer these questions—but not always. In any case, it takes a lot of manual effort and guesswork to try and determine an accurate representation of settlement amounts and other factors. AI can help teams refine their numbers, help spot outliers and cases that have the potential to cost more, and answer the aforementioned critical questions as well as possible, right out of the gate.

Here again, predictive analytics can help. It can leverage machine learning to offer succinct recommendations on how (or even if) to proceed with litigation. Predictive analytics can also offer recommendations regarding budgets, settlement amounts, and more. The more cases that are worked, the more data the system can collect and interpret, resulting in even more accurate recommendations. 

This is only the tip of the iceberg, and there’s a wealth of other benefits that AI and predictive analytics bring to the table. If you’d like to learn more, be sure to download our whitepaper, AI in Corporate Legal: Meeting Challenges and Predicting the Future and check out Vince Venturella’s blog post, The Future of Claims Litigation Technology: Predictive Analytics.


About The Author

Jeffrey Solomon

Jeffrey leads the ELM Solutions Data Analytics and Artificial Intelligence product line. Prior to joining Product Management, he held positions across professional services, consulting, and technology within ELM Solutions and Elevate Services. Jeffrey has nearly 20 years of experience working with legal professionals to drive technology value, solve challenges, and create operational efficiencies.

Jeffrey received his Juris Doctor from Quinnipiac University School of Law and is admitted to practice law in Connecticut.