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Key Takeaways from LFJ’s Virtual Town Hall: Spotlight on AI & Technology

By John Freund |

Key Takeaways from LFJ’s Virtual Town Hall: Spotlight on AI & Technology

On Thursday, February 27th, LFJ hosted a virtual town hall on AI and legal technology. The panel discussion featured Erik Bomans (EB), CEO of Deminor Recovery Services, Stewart Ackerly (SA), Director at Statera Capital, David Harper (DH), co-founder and CEO of Legal Intelligence, and Patrick Ip (PI), co-founder of Theo AI. The panel was hosted by Ted Farrell, founder of Litigation Funding Advisers.

Below are some key takeaways from the discussion:

Everyone reads about AI every day and how it’s disrupting this industry, being used here and being used there. So what I wanted to ask you all to talk about what is the use case for AI, specific to the litigation finance business?

PI: There are a couple of core use cases on our end that we hear folks use it for. One is a complementary approach to underwriting. So initial gut take as to what are potentially the case killers. So should I actually invest time in human underwriting to look at this case?

The second use case is a last check. So before we’re actually going into fund, obviously cases are fluid. They’re ever-evolving. They’re changing. So between the first pass and the last check, has anything changed that would stop us from actually doing the funding? And then the third more novel approach that we’ve gotten a lot of feedback

There are 270,000 new lawsuits filed a day. Generally speaking, in order to understand if this lawsuit has any merit, you have to read through all the cases. It’s very time consuming to do. Directionally, as an application, as an AI application, We can comb through all those documents. We can read all those emails. We can look through social and digest public information to say, hey, these are the cases that actually are most relevant to your fund. Instead of looking through 50 or 100 of these, these are the top 10 most relevant ones. And we send those to clients on a weekly basis. Interesting.

I don’t want you to give up your proprietary special sauce, but how are you all trying to leverage these tools to aid you and deliver the kind of returns that LPs want to see?

SA: We can make the most effective use of AI or other technologies – whether it’s at the very top of the funnel and what’s coming into the funnel, or whether it’s deeper down into the funnel of a case that we like – is that we try to find a way to leverage AI to complement our underwriting. We think about it a lot on the origination side just making us more efficient, letting us be able to sift through a larger number of cases more quickly and as effectively as if we had bodies to look through them all, but also to help us just find more cases that may be a potential fit.

In terms of kind of the data sources that you rely on. I think a question we always think about, especially for kind of early stage cases is, is there enough data available? For example, if there’s just a complaint on file, is that going to give you enough for AI to give you a meaningful result?

I think most of the people on this call would tell you duration is in a lot of ways the biggest risk that funders take. So what specific pieces of these cases is AI helping you drill down into, and how are you harnessing the leverage you can access with these tools?

DH: We, 18 months ago or so, in the beginning of our journey on this use case in law, were asked by a very, very big and very well respected personal injury business in the UK to help them make sense of 37,000 client files that they’d settled with insurers on non-fault motor accident.

And we ran some modeling. We created some data scientist assets, which were AI assets. And their view was, if we had more resources, we would do more of the following things. But we’re limited by the amount of people we’ve got and the amount we get per file to spend on delivering that file. So we developed some AI assets to investigate the nearly 40,000 cases, what the insurers across different jurisdictions and different circumstances settled on.

And we, in partnership with them, improved their settlement value by 8%. The impact that had on their EBITDA, etc. That’s on a firm level, right? That’s on a user case where a firm is actually using AI to perform a science task on their data to give them better predictive analysis. Because lawyers were erring on the side of caution. they would go on a lowball offer because of the impact of getting that wrong if it went to court after settlement. So I think for us, our conversations with financiers and law firms, alignment is key, right? So a funder wants to protect their capital and time – the longer things take, the longer your capital’s out, the potential lower returns.

AI can offer a lot of solutions for very specific problems and can be very useful and can reduce the cost of analyzing these cases, but predictive outcome analysis requires a lot of data. And so the problem is, where do you get the data from and how good is the data? How unstructured or structured are the data sets?

I think getting access to the data is one issue. The other one is the quality of the data, of course, that you put into the machine. If you put bad data in a machine, you might get some correlations, but what’s the relevance, right? And that’s the problem that we are facing.

So many cases are settled, you don’t know the outcome. And that’s why you still need the human component. We need doctors to train computers to analyze medical images. We need lawyers and people with litigation experience who can tell a computer whether this is a good case, whether this is a good settlement or a bad settlement. And in the end, if you don’t know it because it’s confidential, someone has to make a call on that. I’m afraid that’s what we have to do, right? Even one litigation fund or several litigation funders are not going to have enough data with settlements on the same type of claim to build a predictive analytical model on it.

And so you need to get massive amounts of data where some human elements, some coding is still going to be required, manual coding. And I think that’s a process that we’re going to have to go through.

You can view the full panel discussion here.

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John Freund

John Freund

Commercial

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Burford Covers Antitrust in Legal Funding

By John Freund |

Burford Capital has contributed a chapter to Concurrences Competition Law Review focused on how legal finance is accelerating corporate opt-out antitrust claims.

The piece—authored by Charles Griffin and Alyx Pattison—frames the cost and complexity of high-stakes competition litigation as a persistent deterrent for in-house teams, then walks through financing structures (fees & expenses financing, monetizations) that convert legal assets into budgetable corporate tools. Burford also cites fresh survey work from 2025 indicating that cost, risk and timing remain the chief barriers for corporates contemplating affirmative recoveries.

The chapter’s themes include: the rise of corporate opt-outs, the appeal of portfolio approaches, and case studies on unlocking capital from pending claims to support broader corporate objectives. While the article is thought-leadership rather than a deal announcement, it lands amid a surge in private enforcement activity and a more sophisticated debate over governance around funder influence, disclosure and control rights.

The upshot for the market: if corporate opt-outs continue to professionalize—and if boards start treating claims more like assets—expect a deeper bench of financing structures (including hybrid monetizations) and more direct engagement between funders and CFOs. That could widen the funnel of antitrust recoveries in both the U.S. and EU, even as regulators and courts refine the rules of the road.

Almaden Arbitration Backed by $9.5m Funding

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Almaden Minerals has locked in the procedural calendar for its CPTPP arbitration against Mexico and reiterated that the case is supported by up to $9.5 million in non-recourse litigation funding. The Vancouver-based miner is seeking more than $1.06 billion in damages tied to the cancellation of mineral concessions for the Ixtaca project and related regulatory actions. Hearings are penciled in for December 14–18, 2026 in Washington, D.C., after Mexico’s counter-memorial deadline of November 24, 2025 and subsequent briefing milestones.

An announcement via GlobeNewswire confirms the non-recourse funding arrangement—first disclosed in 2024—remains in place with a “leading legal finance counterparty.” The company says the financing enables it to prosecute the ICSID claim without burdening its balance sheet while pursuing a negotiated settlement in parallel. The update follows the tribunal’s rejection of Mexico’s bifurcation request earlier this summer, a step that keeps merits issues moving on a consolidated track.

For the funding market, the case exemplifies how non-recourse capital continues to bridge resource-intensive investor-state disputes, where damages models are sensitive to commodity prices and sovereign-risk dynamics. The disclosed budget level—$9.5 million—sits squarely within the range seen for multi-year ISDS matters and underscores the need for careful duration underwriting, including fee/expense waterfalls that can accommodate extended calendars.

Should metals pricing remain supportive and the tribunal ultimately accept Almaden’s valuation theory, the claim could deliver a meaningful multiple on invested capital. More broadly, the update highlights steady demand for funding in the ISDS channel—even as governments scrutinize mining concessions and environmental permitting—suggesting that cross-border resource disputes will remain a durable pipeline for commercial funders and specialty arbitrations desks alike.

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Unlike litigation finance, where returns are tied to legal outcomes, these loans are secured by awarded contracts or accounts receivable from government entities. Legalist sees overlap in risk profiling, having already built underwriting systems around uncertain and delayed payouts in the legal space.

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