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Key Takeaways from LFJ’s Virtual Town Hall: 2024 Recap & 2025 Outlook

By John Freund |
December 16, 2024

Key Takeaways from LFJ’s Virtual Town Hall: 2024 Recap & 2025 Outlook

Last week, LFJ hosted its final virtual town hall of the year which covered an array of key developments and trends in the legal fundng sector. Panelists included Tets Ishikawa (TI), Managing Director of LionFish, Boris Ziser (BZ), Co-Head of the Finance Group at Schulte Roth and Zabel, William Marra (WM), Director at Certum Group, and Sarah Johnson (SJ), Head of the Litigation Investing Team at The D.E. Shaw Group. The panel was moderated by Rebecca Berrebi (RB), Founder and CEO of Avenue 33, LLC.

Below are the key takeaways from the event.

RB: What are the key changes that have effected the regulatory landscape of litigation finance in 2024, and how do you think those changes have affected deals in the industry this year?

TI: There’s been quite a few symbolic moments over the past two years. There was a proposal [The Voss Report] saying that litigation funding should be regulated and there should be a cap on fees. In the UK, there as a Supreme Court decision in the case of PACCAR that considered litigation funding agreements to be damages-based agreements, basically making a lot of litigation funding agreements unenforceable. And that has triggered an industry-wide review of the litigation funding industry in the UK by the Civil Justice Council. And that is ongoing, with a report expected next year, and the government may act on those recommendations and enact legislation.

In addition to all of that, there was a report written by the European Law Institute, which is probably the most interesting thing to focus on. Rather than the usual high level narratives of what’s good and bad about litigation funding, it actually proposed principles on the back of research and feedback that it got on all sides of the argument. And it was written by some really highly regarded judges and academics. And the report was quite balanced. But what was really interesting about the report was that it set a tone for the direction of how the UK should really be thinking about litigation funding. The key themes coming out of it are that 1) there is no one size fits all solution-litigation funding has many different parts to it, and 2) that regulation is not just something one does, but there needs to be a real identifiable problem that regulation resolves, otherwise there could be a lot of adverse consequences, and that recognition is key. There is also the recognition that funders do run commercial businesses, so there has to be an economically viable solution.

RB: Deal structures evolve as time goes on, and certainly have evolved in our industry. Boris, can you speak to any particular deal structures that have become less popular this year than they were before, or have started to fall by the wayside?

BZ: I wouldn’t say any have fallen by the wayside, I think that there has been a little bit of a shift – if you go back a number of years, you would see there were more debt deals than equity deals, and that was for various reasons, some of it was preference, some was tax-driven, some was based on an analysis of whether you would be splitting legal fees and things like that – and I think over the last couple of years, you have seen more of a shift where more parties are comfortable with equity deals, particularly with the introduction of alternative business structures in Arizona and Utah. So I don’t think that anything has gone by the wayside, but there has been more comfort and more development on the equity side of the business.

RB: Will, do you see that too? What do you think about that?

WM: Yeah I think that’s right. What’s interesting is, there hasn’t been that much development on the question of which provisions in litigation funding contracts may or may not be enforceable, or the big question of tax clarity. I think Boris makes a very good point about Rule 5.4, the debate around that has largely settled. So you do see an increase around law firm deals. I think this question is also tied up with the increasing diversification of products available, and if you start too think about insurance, and insurance-backed debt, and debt plus equity in these deals, we’re seeing a lot of that. We’re also seeing an increase in acquisitions to the extent that claims are alienable and can be acquired. I think that a lot of claim holders are seeing a lot of benefits entering into those sorts of arrangements.

RB: Sarah, what deal structures do you think are growing in popularity, and why do you think that is happening?

SJ: We’ve seen something similar in the shift from debt to equity. I might characterize it though as a move away from debt to law firms, where your collateral is a lot of cases. I think we’ve seen those deals – especially the ones that happened before Covid – there were a lot of different risks that were introduced rather than just the underlying litigation. The amount of OpEx that the law firm needed to survive, and when you’re debt financing for the whole firm, it gets very complicated. So we’ve seen a shift away more to – I won’t say single cases – but perhaps smaller portfolios with a law firm, so you can target your exposure and share more of the risk and OpEx with the law firms themselves.

We’ve also seen a bifurcation in terms of the size of deals. We’re seeing some more very large deals, like $100MM+ deals, and also small single cases, than perhaps we saw in previous years. We’re just seeing a lot of one-off single case deals where funders can share the risk, vs. entire portfolio monetizations.

To view the entire discussion, join the event page on LinkedIn (you must register for the event to view).

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John Freund

John Freund

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Commercial

Which AI Model Is Best for Legal Work? What 2026 Research Says About Accuracy

Law firms, funders and legal departments are being sold AI for contract review, legal research and citation checking, and the models change every few months. So we looked only at independent studies published in 2026 that tested the current generation of models from OpenAI, Anthropic and Google on real legal tasks. The short version: the best models are now genuinely good at reading and extracting from documents you give them, still unreliable at recalling law from memory, and the commercial legal research tools lag behind the best custom systems.

At a Glance

Best overall model for legal document work: Google's Gemini 3.1 Pro. It was at or near the top in every 2026 study that tested it, and it was usually the fastest and cheapest of the leaders. OpenAI's GPT-5.5 found slightly more errors in contract review, and Anthropic's Claude models were the most careful about not flagging problems that weren't there.

Best accuracy recorded on a full legal task: 92%, on a 50-state statutory research test run by Stanford, achieved by a purpose-built research tool. The lesson is that how the AI is set up matters as much as which model sits underneath it.

Range of accuracy: from under 7% (asking a model to recall exact case citations from memory) to 99–100% (catching a citation to the wrong case when the model can read the source). Most real-world document tasks landed between 60% and 85%.

Westlaw and Lexis AI: 58% and 64% accuracy on a Stanford statutory survey test, below a custom-built tool at 83–92%.

Biggest single improvement: giving the model the actual documents instead of asking it from memory cut fabricated citations from roughly 15–40% to about 4–15%, and to under 0.2% with a well-built retrieval system.

Key Takeaways

The model to use. For contract review and extraction, start with Gemini 3.1 Pro. It matched the top performer on catching contract errors (74% vs. 75%) at about one-seventh of the cost and in 90 seconds instead of nine minutes. If catching every possible issue matters more than time or cost, GPT-5.5 with reasoning turned on found the most. For checking citations in a brief, the best 2026 results came from GPT-5 running as an agent and from Claude Code with Claude Opus, which was the most precise.

How to Use It

  • Give it the documents. Never ask a model to supply case law or citations from memory.
  • Turn on the model's "reasoning" or "thinking" mode for review work. It improved error-catching by 9 to 11 points in contract review.
  • Use it as a first pass and a second reviewer, not the final reviewer.
  • For research, use a tool that pulls from a full, current database of the law, because weak retrieval, not the model, causes many of the errors.

What to Expect

  • Contract extraction (pulling out dates, parties, termination and liability terms): about 80–84% accuracy for the best models.
  • Final contract proofreading (defined terms, cross-references, inconsistent language): the best models catch about three-quarters of errors. On a 60-page agreement, expect it to miss some.
  • Citation checking: nearly all citations to the wrong case get caught, but wrong pinpoint pages slip through 20% to 60% of the time.
  • Research answers grounded in the right documents: roughly 6–11% of answers still contain an unsupported statement.

What to Look Out For

  • Citations from memory. When asked to recall exact citations without sources, even the best model scored under 7 out of 100, and 20 of 21 models gave confident, wrong answers more than 94% of the time.
  • Right case, wrong page. Models tend to approve a citation because the case is on the right topic, even when the cited page doesn't support the point.
  • Questions with a false premise. If your question assumes something that isn't true, models often go along with it.
  • Legal research tools' marketing. Westlaw AI and Lexis+ AI trailed a custom-built tool by 19 to 25 points on a Stanford test.
  • Studies funded by vendors. Some of the best-looking results come from companies selling legal AI. Check who ran the test.

Best Practices

  • Ground every task in source documents, and require the model to quote the passage it relied on.
  • Check every citation yourself at the pinpoint page before filing. Automated checkers help but don't replace this.
  • Turn on reasoning mode for review tasks and accept that it's slower.
  • Test a model on a few of your own documents before rolling it out. Rankings change by task.
  • Re-test when a new model version arrives; this field moves in months, not years.
  • Keep a human reviewer accountable for anything that goes to a court, a client or a counterparty.

What the Studies Found

Contract proofreading. In August 2026, researchers had experienced lawyers plant errors in contracts (misused defined terms, wrong cross-references, wrong party names, contradictions) and tested ten current models on catching them. GPT-5.5 caught 75% of errors, Gemini 3.1 Pro 74%, Claude Sonnet 4.6 69% and Claude Opus 4.7 62%. GPT-5.5 cost $1.38 per contract and took about nine minutes; Gemini 3.1 Pro cost $0.19 and took about 90 seconds. Turning on reasoning mode added 9 to 11 points. Every model was far cheaper than a lawyer, and none was close to perfect.

Contract extraction. A May 2026 study tested models on pulling 26 standard fields out of contracts. Among the major models, Gemini 3.1 Pro scored highest (82%), with Claude Opus 4.6 (82%) and Claude Sonnet 4.6 (80%) close behind and GPT-5.4 at 78%. A smaller legal-specific model built by the study's authors scored 84% at far lower cost. The authors work for Onit, which makes that model.

Made-up citations and facts. A January 2026 study had expert reviewers check 2,700 legal answers from 12 models. Asked without source documents, the best models (GPT-5.2 and Gemini 3.0 Pro) cited something false about 15–17% of the time, and the worst over 30%. Giving the models the relevant documents cut that to about 4–15%. A more carefully built retrieval system brought it below 0.2% for every model.

Research with sources. A March 2026 study found that when models answer from retrieved legal texts, Gemini 3.1 Pro produced unsupported statements 5.7% of the time versus 11.3% for GPT-5.2, and that the quality of the search step mattered more than the choice of model. Its authors sell the search component that performed best. An August 2026 study of eight research setups found unsupported answers ranging from under 10% for the best to nearly half for the worst, with the worst results on questions built on a false assumption.

Westlaw and Lexis. In a February 2026 Stanford study, researchers tested legal AI tools against a Department of Labor survey of state unemployment insurance laws. Westlaw AI scored 58% and Lexis+ AI 64%, while a custom statutory research tool scored 83%, rising to 92% after the researchers found that some of its "errors" were gaps in the government's own survey.

Citation checking. A June 2026 study found more than 1,000 court filings containing fabricated citations, a number growing every year, and tested AI checkers on catching them. GPT-5, working as an agent that looks up cases, caught 83% of planted errors; Claude Code running Claude Opus 4.8 was the most precise and scored best overall. No model reliably caught wrong pinpoint cites, partly because page numbers often sit behind Westlaw and Lexis paywalls. A separate August 2026 study found models catch 93–100% of citations to the wrong case but miss many citations to the wrong page, and even GPT-5.4 with full reasoning missed 40% of wrong pinpoints in court opinions.

Citations from memory. A May 2026 study built from 1,000 real U.S. judicial opinions asked 21 models to recall exact case citations without any sources. The best, Claude Sonnet 4.5, scored under 7 out of 100.

The Bottom Line

The 2026 research is consistent: today's best models, led by Gemini 3.1 Pro, GPT-5.5 and Claude, are useful and cheap for first-pass contract review and extraction when they work from the documents in front of them. They still invent law when asked from memory and still miss wrong pinpoint citations, so a lawyer has to verify anything that leaves the building.

Sources (All 2026)

  • Bang et al., "ContractScrub: A benchmark for final review of legal contracts" (Aug. 2026), arXiv:2608.20204
  • Lincoln et al., "A Few Good Clauses: Comparing LLMs vs Domain-Trained Small Language Models on Structured Contract Extraction" (May 2026), arXiv:2605.05532
  • Dantart, "Reliability by design: quantifying and eliminating fabrication risk in LLMs" (Jan. 2026), arXiv:2601.15476
  • Butler and Butler, "Legal RAG Bench: an end-to-end benchmark for legal RAG" (Mar. 2026), arXiv:2603.01710
  • Das et al., "How Much Do Legal RAG Systems Still Hallucinate?" (Aug. 2026), arXiv:2608.14210
  • Afane et al., "Benchmarking Legal RAG: The Promise and Limits of AI Statutory Surveys" (Feb. 2026), arXiv:2603.03300
  • Liu, Stammbach and Henderson, "Who Checks the Citations? Benchmarking Legal Hallucination Detection" (June 2026), arXiv:2606.21155
  • Verma, "Is this Citation on Point?" (Aug. 2026), arXiv:2608.12571
  • Chen et al., "LegalCiteBench: Evaluating Citation Reliability in Legal Language Models" (May 2026), arXiv:2605.10186
Commercial

Second Circuit Affirms Fee Award That Treated Litigation Funding Costs as Firm Overhead

The Second Circuit has upheld a $4.8 million attorneys' fee award in a sex trafficking case, endorsing a district court's decision to strike time counsel spent communicating with its litigation funder.

In Moore v. Rubin, decided on September 4, a panel of Chief Judge Lohier and Judges Parker and Chin affirmed the award to six plaintiffs who won a $3.85 million jury verdict against former bond trader Howard Rubin under the Trafficking Victims Protection Act. In rejecting the argument that too many timekeepers had been compensated, the panel noted approvingly that the district court had applied a 15% across-the-board reduction and excluded non-compensable tasks, "such as communications with counsel's litigation funder."

The more consequential ruling for funders came below. In February 2025, Judge Brian Cogan of the Eastern District of New York refused to shift roughly $1.84 million in principal and interest owed to a third-party funder, reasoning that how a lawyer finances a practice is irrelevant to the client and the defendant alike. "Whether it is a bank loan, family loan, personal assets, or a litigation funder," he wrote, "it is overhead."

Judge Cogan also declined to follow the English decision in Essar Oilfield Services v. Norscot Rig Management, which allowed recovery of funding costs, observing that neither the statute nor the local rule hints at such recovery.

The funding cost denial was not before the appellate panel, as Rubin appealed only the fee award. The funder, Pravati Investment Fund IV, later sought unsuccessfully to intervene to protect its interest in the fees after the plaintiffs' firm dissolved.

Commercial

Legalist Asks Manhattan Federal Court to Confirm $108,718 Award Against Funded Claimant

Litigation funder Legalist has asked a federal court in Manhattan to confirm an arbitration award against a claimant it financed, in a rare public dispute between a funder and the plaintiff whose case it paid for.

As reported by Bloomberg Law, Legalist filed its petition on September 8 in the Southern District of New York, seeking to confirm a partial final award of $108,718.45 against Mario Rinaldi. The award, issued on May 18 by JAMS arbitrator the Hon. Elaine Rushing (Ret.), consists of $105,218.45 in attorneys' fees and $3,500 in arbitration costs.

The funding agreement dates to June 2018 and financed Rinaldi's suit against two French champagne producers, which he brought after working to build their brand in New York. A jury returned a $1.5 million verdict in his favor in March 2022, and final judgment with prejudgment interest was entered that December at $2,318,506.85. That judgment has not been collected. Rinaldi told Bloomberg Law he is still pursuing the money with his own resources, having retained French counsel to enforce it abroad.

Legalist alleges in its filing that Rinaldi breached the funding agreement by refusing to cooperate in collection efforts, including by declining to permit communication with his French counsel. According to the memorandum, Rinaldi did not appear in the arbitration at any stage, and has not moved to vacate or modify the award.

The arbitration was held open for Legalist to pursue further relief, indicating the $108,718.45 may not represent the full extent of its claim. No response from Rinaldi appears in the most recent public docket entries.