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Lexolent Litigation Fund 1 SP Achieves First Successful Investment Conclusion, Delivering Access to Justice in Landmark DIFC Case

August 12, 2025

Lexolent Litigation Fund 1 SP Achieves First Successful Investment Conclusion, Delivering Access to Justice in Landmark DIFC Case

Lexolent Litigation Fund 1 SP, the inaugural fund from litigation funding disruptor Lexolent, and the first litigation fund to be based in the UAE, has achieved its first successful investment in a case litigated before the Dubai International Financial Centre (DIFC) Courts. The matter—Claim No. CFI 081/2023, concerned an unpaid commission claim by Dubai based businessman, Michael Forbes.

Absent Lexolent’s funding, Mr Forbes would have been unable to pursue the case and secure the payment to which he was rightfully entitled. The investment, which was concluded over just 21 months, will generate a very high internal rate of return (IRR) for Lexolent’s Limited Partner (LP) investors, showcasing the fund’s ability to deliver both strong financial performance and tangible social impact.

The result was a resounding success for both parties. Lexolent secured a strong return on its investment, while Mr Forbes obtained a substantial and life-changing judgment in his favour.

“Without Lexolent’s help, I would not have been able to right the wrong that was done to me,” said Mr Forbes. “Lexolent gave me access to justice, and I am delighted to have been introduced to them. I have learned through this experience that not all litigation funders are the same. Nick Rowles-Davies is very much one of the original founders of this industry and is exceptionally easy to work with. His expertise and experience made this transaction straightforward and highly professional.”

Lexolent CEO, Dr Nick Rowles-Davies, commented: “This is a perfect example of litigation funding in action. Without our investment, Mr Forbes would not have been able to secure such a substantial and transformative judgment. It was our pleasure to assist him—and, from our perspective, it was also a very strong investment, particularly given the high IRR that will be achieved for our LPs over a short 21-month period.”

This first win for Lexolent Litigation Fund 1 SP marks a significant milestone for the company as it continues to reshape the litigation finance landscape both in the Middle East and globally. The case underscores the vital role litigation funding plays in levelling the playing field between claimants and well-resourced defendants, ensuring that justice is not a privilege but a right accessible to all.

Syed Mujtaba Hussain, founding partner of UAE based boutique law firm Emirates Legal, acted for Mr Forbes and instructed David Parratt KC and William Frain-Bell KC.

Mr Hussain commented: “This was the first time I have used litigation funding but I will certainly do so again. Lexolent were easy to work with and allowed the lawyers to do their job without concern over fees being met. Litigation funding is a valuable tool and it assisted in producing a great result for Mr Forbes. We are all delighted with the outcome.”

About Lexolent:

Lexolent is a globally coordinated network for legal finance professionals and the first litigation fund to be based in the UAE, offering innovative funding solutions and unmatched expertise in litigation finance. Led by industry pioneer Dr Nick Rowles-Davies, Lexolent connects capital providers with high-value legal claims, delivering results for claimants and investors alike.

Commercial

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Commercial

ILFA Urges Government to Clarify Rather Than Rebuild the Opt-Out Collective Actions Regime

The International Legal Finance Association has filed its response to the UK Government's consultation on competition redress, arguing that reforms intended to speed up the opt-out collective actions regime must not add cost or complexity that makes meritorious claims harder to fund.

The submission responds to the Department for Business and Trade's consultation on "Swifter and Simpler Competition Redress, Regulatory Appeals and Competition Enforcement," which opened on 17 July and closed on 25 September. ILFA's central argument is that the Competition Appeal Tribunal and the appellate courts have already developed workable mechanisms for overseeing class representative suitability, and that the Government should deliver clarity through guidance and the formalisation of existing practice rather than new statutory or procedural requirements.

"Third-party litigation funding is the cornerstone of the opt-out collective actions regime," said Neil Purslow, Chairman of the Executive Committee of ILFA. "Without it, consumers and small businesses would have no realistic means of bringing meritorious claims against well-resourced defendants. In our response, we make it clear that any new reforms must not inadvertently introduce cost or complexity, which only serve to make valid claims harder to bring."

ILFA ties the Government's proposal to permit damages-based agreements in collective proceedings to the unresolved question of funder returns. "Crucially, the Government's proposal to permit damages-based agreements in collective proceedings underscores the urgent need to reverse the PACCAR ruling retrospectively," Purslow said. "To keep this regime viable and investable, we must give funders earlier certainty over returns and introduce better cost budgeting to rein in unpredictable, disproportionate costs."

On costs, the association supports mandatory costs budgeting for claimants and defendants alike from certification onwards, and greater use of alternative dispute resolution where it is required early and backed by real costs sanctions. It also backs a central CAT website for claims and settlements, while cautioning that efficiency measures such as reduced panel composition may yield only marginal savings.

"Maintaining a true equality of arms is essential," Purslow said. "Large defendants should not be allowed to weaponise structural hurdles to quash meritorious claims and ordinary businesses and consumers must remain empowered to hold the powerful to account."

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.