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Key Takeaways from LFJ’s Virtual Town Hall: Spotlight on Australia

By John Freund |

Key Takeaways from LFJ’s Virtual Town Hall: Spotlight on Australia

On Wednesday October 16th (Thursday the 17th, in Australia), LFJ hosted a virtual town hall titled ‘Spotlight on Australia.’ The event featured Michelle Silvers (MS), CEO at Court House Capital, Stuart Price (SP), CEO and Managing Director of CASL, Maurice Thompson (MT), Global Head of Litigation Funding at HFW, and Jason Geisker (JG), Head of Claims Funding Australia. The event was moderated by Ed Truant, Founder of Slingshot Capital.

Unfortunately, Jason Geisker was unable to join the panel due to technical difficulties. However, the other three panelists covered a broad range of topics relating to litigation funding in Australia. Below are key takeaways from the event:

ET: Australia is a pioneer in the use of litigation finance. Can you provide an overview of the Australian market?

MS: Australia has been involved in litigation funding for over 20 years, since the late 1990s. At the moment it’s an interesting environment, we have listed and private funders, hedge funds, law firms and private insurers. Our market is dominated by litigation funders, not necessarily alternative capital sources, which is what tends to happen overseas. We’ve witnessed the market globalizing with offshore funders entering, and local funders expanding abroad, but a lot of the offshore funders have withdrawn from the market in recent years.

The market is small – Australia’s population is 25-28 million, so you can imagine that the way we operate here is quite different than overseas. We have about 10 players operating in the Australian market at the moment. Our environment is quite different than overseas, it’s smaller and well-knit. We all know each other quite well, we compete for the same cases. It’s fierce competition, and an exciting environment.

ET: In terms of return profile, I ‘ve been privy to a lot of litigation finance resolutions on a global basis, and in my review of the data, it strikes me that Australian funders are some of the best in terms of producing consistent returns, albeit the quantum of financing is a little bit smaller than what you might find in the US. Generally speaking, do you agree with that? And to what would you attribute the performance of Australian funders?

SP: I attribute that to the predictability of outcomes, and that really comes from the jurisdiction being established for a long time. Some of the growing pains that other jurisdictions are having, are dealing with new issues and new laws. Most of our bench that deals with litigation funding and new actions, they were senior and junior lawyers, partners, barristers, and now have become judges. So there is an ingrained knowledge of the system, and an appreciation of the importance of litigation funding to provide access to justice.

That in itself also goes with the Australian civil justice system, which is an absolute Rolls Royce. It is gold-plated, it is costly, so you need to be able to navigate that in a way where duration risk doesn’t become an issue to you. So when you talk about performance, I absolutely agree Australia is up there as one of the better performing markets in the world. We select our cases well and we settle cases before trial (about 95% of cases settle before trial – that brings duration risk down). That combination of factors are all a reflection of the 25 years-plus of existing in this market.

ET: Up until recently, outside of the class action space, lawyers have not been able to engage in contingent fee arrangements, but jurisdictions like Victoria have changed this dynamic. Can you discuss the current state of contingent fee arrangements and its likely trajectory, and the implications for the litigation funding market?

MT: Everything Stuart mentioned about this being an isolated part of the world, and the impacts that has on doing business here, is absolutely correct. A flip on that though, is that degree of isolation that we’ve had as a nation has always had us looking closely outside of our borders. So we observe what’s happening in other parts of the world and that influences how we think.

Some of the comments you’ve heard might suggest that we’re a slightly immature legal market, in the sense that politics have impacted the courts and there has been some degree of uncertainty since 2020. But I’d flip that and say that this is a case of us looking hard at what we need moving forward and what will suit Australia. The largest differential between us and the United States, for instance, is that we never want to see a situation in Australia where the overweight child might sue the fast food chain because some lawyer provides contingent fee arrangements, all those sorts of things. We’ve laughed at that scenario overseas, and we don’t want that here. So the whole idea of contingent fees stirs up all sorts of feelings in our legal environment, and in having to deal with those negative perceptions, we have to think very carefully about how we structure things moving forward.

In the period between 2020 and now, there’s been a proliferation of class actions in Victoria to take advantage of the contingent fee arrangements. Not all law firms have done that – my law firm, for instance, we’re running three large plaintiff class actions at the moment, we’ve got a few others in the pipeline. We’re currently not fixated on Victoria, because among other things, the way it’s been dealt with – generally if you want to take full advantage of a contingent arrangement sanction by the court and legislation, you have to bear all the risk of the costs and a security for costs order against the law firm. And most law firms won’t stomach that at all (because this is so new). But other law firms see this as an opportunity – particularly large national firms like Maurice Blackburn for instance. Large firms like that will take advantage because they can finance the risk. If I’m going to sell that to my partners in London, Asia or elsewhere, it’s a different proposition.

So we are inching closer to a wider opportunity for law firms to take on contingent risk, but we’re not there yet. I don’t think it’s going to be the free for all that people have been concerned about. That’s not to say there hasn’t been class actions flooding into Victoria as opposed to other states, but I think that will slow down. And so a firm like us is looking beyond the Victoria borders.

To view the entire 1-hour discussion, please click here.

About the author

John Freund

John Freund

Commercial

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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

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.

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.