The Case for Data Analytics in Legal Funding
November 2, 2020
Much of the litigation finance industry is still skeptical about data analytics. The CEO of Burford, Christopher Bogart, revealed this industry-wide skepticism in a recent article in which he questioned whether today, analytics can effectively help litigation financiers allocate capital more efficiently. Instead, he posits that the value of AI, and analytics more broadly, pale in comparison to human intervention.
Such skepticism is not new and is certainly not unique to litigation finance; and he may very well be right. But as we'll see, I believe that there are ways that legal funders today can begin harnessing analytics to supplement their current business.
In myriad industries and professions, participants frequently believe their work cannot be improved using data analysis. This misconception has been true in everything from pricing products, stock market investing, and industrial machine maintenance to strategy games like Chess and Go.
However, savvy players like Netflix, Amazon, hedge funds Bridgewater and Renaissance Technologies and retailer Target would all contend that data analytics improve the work product of employees - as does this MIT Sloan study, which found, "Top-performing organizations use analytics five times more than lower performers."
Despite the evidence across many industries in favor of data analysis, legal funders continue to believe their field has no use for it. Reasons cited vary. Some find a lack of data in their space deterring, while others insist that the expertise and skill of experienced attorneys are enough to run an effective legal funding business. There is no doubt that using data as a legal funder is challenging; still, it is just as hard for other professionals in different industries.
Common issues in the analysis of litigation funding data are not unique to legal financing, and have been solved. In order for investors to take legal funding seriously, and so that legal funding can grow as an industry, funders must come to appreciate how data analytics compliments the human component of legal funding, as well as learn how to effectively problem solve using data and analysis.
Litigation Finance's Data Problems are Not Unique or Insurmountable
I recently began a data analytics project for one of my legal funding consultees. This task uncovered one of the main obstacles every business faces in leveraging data analysis: a lack of data.
Specifically, my consultee hopes to use its salespeople more effectively and determine the value of an in-person sales visit. Ideally, the firm would like to prematurely determine which customers warrant a sales visit, and which don't. Unsurprisingly, we have no data. And that's OK. The project involves building the database we need and then doing the analysis.
The point is, it's actually quite rare to have a nice, neat dataset, pre-built, cleaned and ready for analysis. Most of the work in doing data analytics is indeed in putting together a relevant and useful dataset. Just ask General Electric how easy it was to build their new Predix Engine that forecasts maintenance schedules for machines. Most of that data did not exist until GE built it.
The Way Forward for Legal Funding
Litigation finance has only one option if it wants to be more broadly accepted as an asset class, and that is to embrace standardization and objective metrics for valuation, risk and more.
There are no major asset classes that are not standardized. Indeed the difference between major asset classes and niche assets is often a lack of standardization. Of course, first and foremost, standardization requires gathering data, and then eventually, the proper use of that data.
Moreover, litigation finance as a field can benefit tremendously from increased use of data. Data can help litigation finance firms make better investments, assess risk and predict outcomes in existing investments to a higher standard, value claims and forecast returns for portfolios of matters.
Even business operations at litigation finance firms can be improved with data. For example, funds seeking institutional investors can use data analytics to optimize pricing and discounts for investors and clients.
None of this is new or impractical. It is done constantly in other industries.
For example, investment banks use data analytics to help corporate clients project market reactions to particular corporate announcements (e.g. a merger) or help predict demand for securities offerings. Hedge funds use data analytics to value and assess complex illiquid securities, and compliance groups can help corporate execs understand risks from regulators using tools like the SEC's new API measure, or any of the several other data-related flagging tools.
Textual analysis is one of the newer tools in data analytics. It was actually the basis for the recent SEC announcement, as it helped investigate stock manipulation through articles posted to investment website Seeking Alpha. The Seeking Alpha SEC story highlights an important reality for legal funding firms: areas that were once the concern of humans, like reading and analyzing information about legal cases, can now be done effectively by computers.
I know Seeking Alpha very well. I used to be a staff member there. Specifically, I worked on the PRO product, and, among other things, I worked on Big Data projects to help turn their content into useable data for institutional investors. Seeking Alpha is trying to disrupt the traditional model of analyst coverage in finance, but the truth is that financial analysts themselves are being disrupted by advances in data analytics.
And, there is not much separating financial analysts from litigation finance attorneys - they use similar skill sets, as the litigation finance field will eventually realize.
Steps in Data Analytics
To understand data analytics, one must start by understanding the steps that all data analytics and business intelligence processes take. This series will explore those steps as they relate to litigation finance.
There are five steps to any data analytics project:
- Determine the right question to ask, then record your hypothesis.
- Gather relevant data to answer that question.
- Clean and structure that data.
- Run analyses to test your hypotheses.
- Review the data and make a decision.
Next time, we'll look at the nuts and bolts of the first step.