The debate on the value of legal analytics for litigation finance should not be over whether the underlying data sets are viable; the data is available, and especially for federal analytics. The real debate should center on how and why litigation finance should use legal analytics and court data to continue producing substantial rates of return for institutional investors.
The genesis of this article stems largely from an insightful piece on litigation finance by Above the Law’s very own David Lat, and his observation that “…there is a debate in litigation finance about the utility of analytics, given the nature and size of the data set.” Here, we’ll explore how advances in the legal technology space and improved access to data has made meaningful analytics possible and review examples of how analytics can be leveraged in litigation finance.
Show Me the Data
The facts relating to the underlying data are fundamental to any discussion on the viability of legal analytics in the litigation finance world. For starters, multiple legal tech companies collectively pull millions of data points from federal courts on a recurring basis every day. While on the one hand advocating for improved access to data and removing restrictive paywalls, they’re also spending hundreds of thousands of dollars (if not millions collectively) per year purchasing PACER data, and creating mountains of actionable information for a myriad of use cases, including those in litigation finance.
On top of building these mountains of data, legal tech companies are also applying artificial intelligence (i.e., machine learning) to clean up and better structure their data to produce more meaningful analytics. Without the machine learning technologies like normalization, which is used to clean raw data from PACER, it would not be possible to provide accurate analytics on the real-world entities that matter, such as the actual attorneys, parties, and judges involved in a case.
Imagine you want to know the analytics behind one of the countless attorneys named John Smith. With normalization, legal tech companies can not only ensure you are seeing the analytics for your particular John Smith, but they can also identify situations where the court may have misspelled John’s name, or other instances, such as John’s middle initial being included or omitted from court records. Being able to distinguish between and see the correct analytics for the John Smith or Jane Doe handling a case you want to invest in can be crucial when you’re weighing a potential multi-million-dollar investment.
Noting the sheer volume of data available and the continuing advances being made by legal tech companies to better structure and enhance that data, the open question for the litigation finance industry is what data points from those millions pulled should they start tracking consistently to develop analytics and better position their next investments into successful legal claims.
Knowing the Landscape
In the world of litigation finance, there are many factors impacting a decision to invest in a claim, and legal analytics can greatly help to separate the wheat from the chaff. Specifically, legal analytics can greatly enhance the initial due diligence litigation finance companies conduct by providing a better picture of the legal landscape encapsulating a potential claim.
The first question a Director at a litigation finance company might ask when evaluating a claim is: who is this plaintiff asking for my firm to invest in their claim? With legal analytics, litigation finance companies can quickly answer important questions like how many lawsuits has this plaintiff been involved in previously. Do they have a history of being litigious? Do they file frivolous lawsuits that are routinely lost on summary judgment? These are important initial questions to quickly separate the serious claimants from others with less realistic claims, like a recent lawsuit filed in the Southern District of New York seeking $101 billion from Apple and new iPhones and Macbooks for everyone.
Beyond looking at the plaintiff and the merits of their claim, litigation finance companies would also likely want to know about the experience of the attorney(s) hired to represent the plaintiff. Some important questions to ask that legal analytics can easily resolve are: how many cases have they handled altogether, how many cases within the same case type have they handled, and do they have experience in front of the judge/judges who might hear the case? Litigation finance companies can also use legal analytics to determine the length of time the attorney(s) takes to close a case or hit major milestones to better gauge how long it might take to recoup an investment. It would also be important to uncover the outcomes of the cases they’ve handled – do they win on summary judgment often and/or do they tend to reach settlements early on.
Apart from the plaintiff and their counsel, it may also be wise to know more about the defendant. Who are they as a party? Do they get sued often? Who are their go to defense firms – do they have a stable of white shoe law firms ready to defend them to the last? And how good are those firms – what’s their experience for this case type and what outcomes do they typically achieve? It may also be prudent to know whether the defendant has a history of dragging cases out to trial and/or appeal, only to fight again come the time to collect. Even if your plaintiff has an iron clad claim, fits the 10:1 ratio for damages, and has a rockstar for attorney, if you can’t collect without resorting to ultimately filing an involuntary Chapter 11 petition, it’s important to understand the additional time and resources that might be needed to see your investment through to the end.
The types of legal analytics being produced by legal tech companies today can dispose of these questions (and many more) and help litigation finance companies make investment decisions grounded in fact without needing to scour through millions of data points to reach the same results.
Positioning for Success
Reducing risk and increasing returns is a simple but important maxim for investing, and for litigation finance, legal analytics can do both. Knowing when to avoid unwinnable, costly, or protracted litigation through asking and answering some of the basic questions surrounding a legal claim can not only help investors in litigation finance retain what have been remarkably robust returns, but it can also position them to be seen as forward-thinking leaders of a relatively new field when institutional investors ask about their process for selecting which claims to back financially.
But this is just the beginning. Now that we’ve established that the data is available and meaningful analytics are possible for litigation finance, the door is open to leveraging court data for business development to find new lucrative claims and litigation portfolios to invest in, and the introduction of systematic efficiencies to scale their investment operations. Excellent fodder for a future or even ongoing discussion on this rapidly changing landscape.
Jeff Cox is the Director of Content & Data Acquisition for UniCourt, a SaaS offering using machine learning to disrupt the way court records are organized, accessed, and used. Jeff is a Florida attorney, who loves all things legaltech and volunteering with local legal aid programs in Tampa. You can email Jeff at jeffc@unicourt.com, and you can follow him on Twitter @JeffCoxEsq.
