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Litigation finance, big data and the limits of AI

This article by Burford CEO Christopher Bogart was first published on Law360 and is available here

Litigation finance is a fast-growing area of the business of law: Its use by U.S. Law firms quadrupled between 2013 and 2016, according to a 2016 survey. Now that litigation finance is responsible for billions and not just millions of legal spending, it is regularly suggested that it should be at the forefront of innovation in law — and “big data” and artificial intelligence are some prominent current areas of interest.

Theoretically, both better data and its better use should be able to improve results in litigation, and thus help litigation financiers allocate more capital to meritorious matters. To be sure, making litigation more predictable and more efficient are worthy — if lofty — goals. However, while big data and AI are intriguing additions to the litigation toolkit, they are far from turning litigation finance as we know it on its head.

Intriguing, But Still Early Stage

“Big data” is the analysis of large data sets to glean insights and to try to predict likely future outcomes. It has revolutionized fields from law enforcement to disease control to mass marketing.

“AI” is the use of machines to automate human tasks. In law, its most familiar manifestations include analyzing enormous troves of data to isolate relevant information, such as in response to discovery requests, or to unearth patterns or anomalies.

And “litigation finance”? It involves unlocking the asset value of commercial litigation matters to allow a litigant or a law firm to receive capital in advance of a legal outcome, in exchange for a portion of the proceeds of that outcome. Although litigation finance agreements are highly varied, financing almost always is nonrecourse, meaning repayment is contingent upon legal outcomes. As a result, litigation finance providers are extremely selective in their investments; to be otherwise would be foolish.

This selectiveness takes the form of very careful scrutiny of potential matters, and a significant portion of the work of litigation finance involves teams of experienced litigators assessing matters for investment. Beyond immediately homing in on whether matters fit the financier’s business model (for example, the major players finance only commercial disputes), litigation finance underwriters painstakingly assess:

  • Legal Merits — Strong legal merits are essential to secure financing.
  • Counsel — Experienced counsel with demonstrated track records are highly desirable.
  • Counterparty — The firm and/or business must be reasonably financially sound, or there must be a path to protect the investment.
  • Enforceability — Assurance is needed that if the case is successful, the losing party is creditworthy, with assets capable of supporting enforcement.
  • Financial Fit — Not only must there be a sufficient capital need (which for the most well-known financiers is more than $1 million at the low end), the realistic recovery must be able to support the client’s expectations, the law firm’s costs and the financier’s return.

As every lawyer knows, many cases run afoul of one (or more) of those five pillars of litigation finance assessment — but it can take a lot of time to figure that out in diligence and may not even be foreseeable at that point, and perhaps not even until the case is well along the tracks. Thus, anything that improves efficiency and predictability is valuable in litigation finance.

Sadly, at least with respect to complex commercial litigation, it isn’t time yet to relax, sit back and leave litigation assessment to the machines.

Big data and AI work well when there are large amounts of data, routinized scenarios and defined variables. They stumble when confronted with subjective variables and scenarios that are laden with idiosyncratic risk. While theoretically well designed to find the proverbial needle in a haystack, big data and AI currently lack the ability to do so usefully in a commercial litigation financing context. But we continue to be intrigued by their future possibilities.

Data Analytics and Litigation Finance

While litigation finance has been growing rapidly, it remains a tiny portion of the vast global legal economy: Each year, hundreds of billions of dollars are spent on legal fees; millions of litigation claims and other matters involving legal or regulatory risk come into being; and probably trillions of dollars change hands in resolving those claims. Frankly, there remains huge growth potential — and anything that helps increase the pool of qualified matters to be financed is of course a welcome development.

But while data analytics has a place in this growth (more on that below), arguably the larger drivers of growing litigation financing in the short term are dependent on human intervention: They are first, the ongoing development of better products and services, and second, the continued normalization of the field.

To that point, average litigation finance commitments continue to grow: for example, our average commitment at Burford Capital has grown from $3 million to $13 million since our founding in 2009. Additionally, the industry continues to develop more ways of working with clients across broader areas of law. The development of portfolio and other complex solutions, and the extension of financing to defense matters, mergers and acqusitions, tax disputes and other areas of law beyond litigation are areas that human intelligence have identified.

As to the normalization of commercial litigation financing, whereas a few years ago some firms were still expressing a lack of interest in litigation finance based on the assumption that they didn’t “need” it, that is now an exceptional assertion and many firms are regularly engaged in litigation finance discussions as a normal part of their business.

To put it another way: Product development and normalization have together resulted in more needles and more haystacks.

Some new entrants to the field of litigation finance have business models that are explicitly technology-driven or dependent upon data analytics to function. For example, some take a “crowd-sourcing” approach to litigation finance, where investors are solicited for matters that have been pre-vetted by the company. Others seek to expand the pool of matters available to financing to a more mass market by soliciting litigants online and using proprietary algorithms to compare attributes of millions of state and federal cases to cases seeking funding.

Both approaches speak to the healthy growth and evolution of litigation finance. However, private companies with no audited disclosures of financial performance are virtually impossible to assess. It is therefore reasonable to be skeptical of players that are fundamentally based on a model of investing in single litigation matters and therefore are more exposed to binary litigation risk. This directly contrasts with the litigation finance model of investing in pools of multiple linked matters which are able to diversify risk.

Future Innovation in Litigation Finance

However, it is unquestionable that as litigation finance grows and evolves, data analytics will play an increasing role: If nothing else, litigation finance providers have accumulated vast amounts of their own data based on the many thousands of matters they have seen, and built their own proprietary risk management and investment models. As time passes, more data and more evolved models will help litigation finance providers scale their business by broadening scope as well as simply speeding up the underwriting process.

But much as we embrace technological development, we suspect the litigation finance business will for the foreseeable future remain a business that is still more dependent on specialized expertise and human judgment than it is on big data and AI. It’s worth noting that, alongside the capital litigation financiers provide, it’s precisely our human judgment that clients and firms expressly value, as it adds an extra layer of insight based on tremendous accumulated experience. The people who receive our financing regularly tell us how much our team add value given their exposure to billions of dollars of commercial litigation and arbitration matters, a comment more evocative of a consultative, investment banking relationship rather than a transactional model.