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Key Takeaways from LFJ’s Digital Event: Legal Tech and LitFin

December 7, 2023

Key Takeaways from LFJ’s Digital Event: Legal Tech and LitFin

On December 6th, 2023, Litigation Finance Journal produced its final event of the year: Legal Tech and LitFin: How Will Tech Impact Litigation Finance Globally? Tets Ishikawa moderated an insightful and pertinent discussion on the use of legal tech in the litigation finance industry. Panelists included Nick Rowles-Davies (NRD), Founder of Lexolent, Isabel Yang (IY), Founder of Arbilex, and Joshua Masia (JM), Co-Founder and CEO of Dealbridge.ai. Below are some key takeaways from the event (answers have been truncated for the purpose of this article): Legal tech is quite a broad term.  What does the legal tech landscape mean to you, and how does it fit into your business? IY: We’re in a very exciting time in legal tech. Where I sit, I primarily deal with the underlying technology being artificial intelligence (AI). The primary advances in advanced AI have primarily occurred out of language being the source data. A lot of these text-based AI advancements all hold great significance for the practice of law. At Arbilex, we are taking advantage of large language modeling (LLM) to reduce the cost of data acquisition. When we take court briefings and unstructured data and try to turn that into structured data, the cost of that process has dramatically decreased, because of Chat GPT and the latest LLMs. On the flipside, because AI has become so advanced, a lot of off-the-shelf solutions have tended towards a black box solution. So the model’s output has become a more challenging task. At Arbilex, we have always focused on building the most stable AI—so we focus on how we can explain a particular prediction to our clients. We are increasingly investing a lot of our time and human capital into building that bridge between AI and that use case. How relevant has legal tech been, and will it be, in the growth of the litigation finance sector?  JM: When we look at scaling operational processes, a lot of times we have to put our traditional computer science hat on and ask, ‘how have we historically solved these problems and what has changed in the past several years to evolve this landscape?’ A lot of the emphasis with technology has been about normalizing and standardizing how we look at these data sets. There’s a big issue when you look at this approach and what existing platforms have been doing—this is a very human business. Because of that, there’s a lot of ad hoc requests that get mixed in. So what gen-AI is doing, we’re getting to a point where you don’t have to over-structure your sales or diligence process. Maybe the first few dozen questions you’re asking of a given data set are the same, but eventually we want to be able to ask questions that are specific to this deal. So being able to call audibles and ad-hoc analysis of data sets was really hard to do before the addition of generative AI. NRD: Legal tech is becoming increasingly relevant, but the real effect and usefulness has grown over time. It makes repetitive tasks easier, and provides insights that are not always readily apparent. But in terms of the specific use of AI to triage outcoming matters, we identify matters in different areas—is this something we simply aren’t going to assess, will it be sent back for further information, does it fit the bucket of something we would fund per our original mandate, or does it go on the platform for the purpose of others to look at and invest in that particular matter. AI is having an increasing impact and is being used with more regularity by litigation funders who are funding they can increase efficiency and get to a ‘yes’ much more quickly. A lot of lawyers would say, this is fascinating, but ultimately this is a human industry. Every circumstance will be different, because they will come down to the behaviors of human beings in that time. Is there a way that AI can capture behavioral dynamics? IY: In general, we need to have realistic expectations of AI. That comes from, what humans are uniquely good at are not necessarily the things that AI is good at. AI is really good at pattern-spotting. Meaning, if I train the model to look for recurring features of particular cases—say, specific judges in specific jurisdictions, when coming up against a specific type of argument or case—then AI in general has a very good ability to assign the weighting to a particular attribute in a way that humans instinctively can come to the same place, you can’t really quantify the impact or magnitude of a specific attribute. The other thing that we need to be realistic about, is that cases are decided not just on pattern, but on case-specific fact attributes (credibility of a witness, availability of key evidence). If you train AI to look for things that are so specific to one case, you end up overfitting the model, meaning your AI is so good at looking for one specific variable, that it loses it general predictive power over a large pool of cases. What I would caution attorneys, is use AI to get a second opinion on things you believe are a pattern. In arbitration, attorneys might use AI on tribunal matters—tribunal composition. AI models are way better at honing in on patterns—but things like ‘do we want to produce this witness vs. another witness,’ that is not something we should expect AI to predict. For the full panel discussion, please click here.

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Commercial

Litigation Financiers Organize on Capitol Hill

By John Freund |

The litigation finance industry is mobilizing its defenses after nearly facing extinction through federal legislation last year. In response to Senator Thom Tillis's surprise attempt to impose a 41% tax on litigation finance profits, two attorneys have launched the American Civil Accountability Alliance—a lobbying group dedicated to fighting back against efforts to restrict third-party funding of lawsuits.

As reported in Bloomberg Law, co-founder Erick Robinson, a Houston patent lawyer, described the industry's collective shock when the Tillis measure came within striking distance of passing as part of a major tax and spending package. The proposal ultimately failed, but the close call exposed the $16 billion industry's vulnerability to legislative ambush tactics. Robinson noted that the measure appeared with only five weeks before the final vote, giving stakeholders little time to respond before the Senate parliamentarian ultimately removed it on procedural grounds.

The new alliance represents a shift toward grassroots advocacy, focusing on bringing forward voices of individuals and small parties whose cases would have been impossible without funding. Robinson emphasized that state-level legislation now poses the greater threat, as these bills receive less media scrutiny than federal proposals while establishing precedents that can spread rapidly across jurisdictions.

The group is still forming its board and hiring lobbyists, but its founders are clear about their mission: ensuring that litigation finance isn't quietly regulated out of existence through misleading rhetoric about foreign influence or frivolous litigation—claims Robinson dismisses as disconnected from how funders actually evaluate cases for investment.

Commercial

ISO’s ‘Litigation Funding Mutual Disclosure’ May Be Unenforceable

By John Freund |

The insurance industry has introduced a new policy condition entitled "Litigation Funding Mutual Disclosure" (ISO Form CG 99 11 01 26) that may be included in liability policies starting this month. The condition allows either party to demand mutual disclosure of third-party litigation funding agreements when disputes arise over whether a claim or suit is covered by the policy. However, the condition faces significant enforceability challenges that make it largely unworkable in practice.

As reported in Omni Bridgeway, the condition is unenforceable for several key reasons. First, when an insurer denies coverage and the policyholder commences coverage litigation, the denial likely relieves the policyholder of compliance with policy conditions. Courts typically hold that insurers must demonstrate actual and substantial prejudice from a policyholder's failure to perform a condition, which would be difficult to establish when coverage has already been denied.

Additionally, the condition's requirement for policyholders to disclose funding agreements would force them to breach confidentiality provisions in those agreements, amounting to intentional interference with contractual relations. The condition is also overly broad, extending to funding agreements between attorneys and funders where the insurer has no privity. Most problematically, the "mutual" disclosure requirement lacks true mutuality since insurers rarely use litigation funding except for subrogation claims, creating a one-sided obligation that borders on bad faith.

The condition appears designed to give insurers a litigation advantage by accessing policyholders' private financial information, despite overwhelming judicial precedent that litigation finance is rarely relevant to case claims and defenses. Policyholders should reject this provision during policy renewals whenever possible.

Commercial Regulatory Issues

Valve Faces Certified UK Class Action Despite Funding Scrutiny

By John Freund |

The UK Competition Appeal Tribunal (CAT) has delivered a closely watched judgment certifying an opt-out collective proceedings order (CPO) against Valve Corporation, clearing the way for a landmark competition claim to proceed on behalf of millions of UK consumers. The decision marks another important moment in the evolution of collective actions—and their funding—in the UK.

In its judgment, the CAT approved the application brought by Vicki Shotbolt as class representative, alleging that Valve abused a dominant position in the PC video games market through its operation of the Steam platform. The claim contends that Valve imposed restrictive pricing and distribution practices that inflated prices paid by UK consumers. Valve opposed certification on multiple grounds, including challenges to the suitability of the class representative, the methodology for assessing aggregate damages, and the adequacy of the litigation funding arrangements supporting the claim.

The Tribunal rejected Valve’s objections, finding that the proposed methodology for estimating class-wide loss met the “realistic prospect” threshold required at the certification stage. While Valve criticised the expert evidence as overly theoretical and insufficiently grounded in data, the CAT reiterated that a CPO hearing is not a mini-trial, and that disputes over economic modelling are better resolved at a later merits stage.

Of particular interest to the legal funding market, the CAT also examined the funding structure underpinning the claim. Valve argued that the arrangements raised concerns around control, proportionality, and potential conflicts. The Tribunal disagreed, concluding that the funding terms were sufficiently transparent and that appropriate safeguards were in place to ensure the independence of the class representative and legal team. In doing so, the CAT reaffirmed its now-familiar approach of scrutinising funding without treating third-party finance as inherently problematic.

With certification granted, the case will now proceed as one of the largest opt-out competition claims yet to advance in the UK. For litigation funders, the ruling underscores the CAT’s continued willingness to accommodate complex funding structures in large consumer actions—while signalling that challenges to funding are unlikely to succeed absent clear evidence of abuse or impropriety.