CHICAGO — The application of quantitative methods to the delivery of legal services was the theme of Fin(Legal)Tech, a conference held at Chicago-Kent College of Law on November 4. As the name of the conference implies, many of the topics covered at the event included attempts to draw lessons from the financial technology world (FinTech), where the application of quantitative methods is much more of a given.
More than just a legal tech conference, however, this was an attempt to really focus on attacking what conference organizer Daniel Martin Katz, director of The Law Lab at Chicago-Kent, calls the predominant, almost pathological aversion to quantitative techniques in the mainstream legal services industry and in legal education. The legal industry is far behind the financial services industry in developing transformational technology, but many of the TED Talk-style presentations at Fin(Legal)Tech focused on the parallels between the legal and financial realms.
Katz kicked things himself off with an overview of the ways that the technology and quantitative methods that are common in financial review can be applied to legal. But the path to that end-state requires a shift in the way people think about the value proposition that lawyers bring to their clients. He summarized it as follows:
- Transactional lawyers: help to price risk, and reduce information asymmetries.
- Litigators: Help to characterize (predict) risk and exposure, and shift the expected value of a lawsuit.
- Compliance practitioners: identify and prevent rogue behavior, and monitor behavior in (near) real time.
The current model, argues Katz, is that the industry relies on an expert-centered pricing of risk. There is a “cult of one person’s thinking” in the way lawyers assess risk today. Katz would like to broaden the methodology from that reliance on experts, to a reliance instead on crowds and algorithms as ways to assess risk, as practiced in the financial and insurance space.
The FinTech Approach to Legal
The wide-ranging conference program was an exercise in identifying applications within the legal industry for the same techniques that FinTech does well: quantitative risk assessments and business practices, and the use of computing to reduce friction in legal transactions.
Here’s a sampling of takeaways from the more than 30 presentations:
Legal Operations Leveraging Data
Bruce Goldberg from Allstate and Aaron Katzel from AIG provided examples of how today’s in-house law departments are leading the way with quantifiable goals, budgets and metrics. In managing more than 1,200 outside firms, AIG has created an online sourcing platform that conducts automated requests for information, online RFPs, and reverse auctions conducted in real time. AIG has estimated it has seen $950 million in legal savings between 2012 and 2016 on a total annual budget of $2.4 billion.
Decision Trees for Measuring Value
A pervasive thread running through the conference was the large number of times that decision trees were referenced as tools. Marc Van Allen of Jenner & Block demonstrated how decision trees can be used to model the Economic Value Added (EVA) provided by lawyers, in order to determine the optimal amount of resources to invest in a given litigation. EVA is the total cost of a litigation, minus liability and cost of defense.
Using decision trees as models is the key to this — by showing a possible distribution of outcomes, EVA can be predicted with some level of precision. A collection of decision trees becomes a portfolio that can be further analyzed for making substantive and procedural predictions about outcomes. Van Allen identified three ways to improve EVA once it’s measured: Sequencing (focusing on learning bad news early, and delaying investment until after the bad news is understood); Value Engineering (testing various outcomes); and Playing the Odds (making better settlement decisions by not accepting settlement offers lower than expected trial results).
A trio of representatives of litigation finance firms provided examples of the financialization of the law in its most literal sense. Selvyn Seidel of Fulbrook Capital Management, Travis Lenkner of Gerchen Keller Capital, and James Batson of Bentham IMF all provided insights into the growing field of litigation finance.
In various forms, litigation finance has been around for centuries (contingency fees and litigation insurance are early forms), but in recent years the availability of technology and analytics has led to a booming market, as investors get better at measuring risk, and can invest in portfolios of cases rather than individual cases.
The growth of alternative fee arrangements (AFAs) is pushing litigation finance forward, as firms use litigation finance as a hedge against the risk of taking on large cases on an AFA basis. Increasingly litigation finance has been embraced by both firms and clients, and it’s not hard to see why. Clients get not only funding but an outside risk assessment of their case from a third party, and firms can “securitize” a portfolio of cases and free that money to finance other operations and investments.
Frictionless Delivery of Law
Just as Katz identified the “removal of socially meaningless friction” from financial markets as one of the drivers of the FinTech industry, so too are new players busy using technology and computation to reduce friction in law.
Here the prominent examples come from the public-facing court systems and from legal tech companies enabling consumers to access justice. MJ Cartwright of Court Innovations demonstrated how the public-facing systems in courts can be revamped with technology that removes friction and churn from high-volume, low-value transactions such as warrants and fines.
Jessica Frank of Chicago-Kent’s Center for Computer-Assisted Legal Instruction (CALI) showed how its Access to Justice (A2J) Author platform has been used to conduct 3.5 million guided interviews in legal aid contexts. Mark O’Brien of Pro Bono Net showed how its Citizenshipworks initiative takes a citizenship process that is designed for lawyers, and turns it into one more easily navigated by end-users and the “community navigators” they are already working with such as educators, social services agencies and churches.
“Law is Computation:” Blockchain and Computable Contracts
A session on blockchain and computable contracts felt like a description of the future rather than Fin(Legal)Tech in the here and now, but it also seems no less inevitable as a field for future innovations.
Oliver Goodenough of Vermont Law School laid out the basics: law already is a form of computation, but it uses natural language (legalese) as its platform. Contracts can also be represented in computer language, however, and processed in a series of if/then/else statements. While natural language contracts can get away with a certain degree of imprecision and lack of standardization, computers will require more precise, standardized language, as well as Scratch-like tools for building contracts out of standard components. Blockchain was also well represented in this session. Nina Kilbride of Monax described blockchain contracts as a set of “ecosystem applications” that will constitute the next wave of process automation in law.
This was not your usual legal tech conference that focused on new technologies and their transformational effects on the business or practice of law. The over-arching focus at Fin(Legal)Tech was not on the technology, but on seeing law in a new way — as a discipline and an industry that, like the financial services industry, can be transformed by applying new techniques.
Those new tools include: measuring and generating data out of transactions; applying analytics to that data; pricing and predicting risk; eliminating friction with new processes; and turning natural-language transactions into computable ones. The innovations and practices documented here, as they become more mainstream, will require lawyers to think more and more in terms of mathematics and computation.
Most of this stuff is here today, and that change of mindset isn’t something that most legal organizations can delay much longer.