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Commercial Covid-19

Investor Caution in the Wake of a Hard Insurance Market

The insurance industry is facing a hard market thanks to multiple factors including the COVID pandemic. Hard markets are a time of high insurance premiums, more precise and complex underwriting, fewer policies being written, and a shrinking pool of competitors. With that in mind, insurers are raising money to make the most of opportunities as they arise. At the same time, investors are understandably cautious.

Intelligent Insurer details a recent panel discussion on hard markets with commentary from Stefan Holzberger of AM Best and Jon Warwick of ILS Capital. The experts predict how investors may respond to hard market conditions and how that will impact the insurance industry in the coming months.

Holzberger notes that factors affecting the market cycle include low-interest rates, loss creep from previous catastrophic events, and litigation finance. He predicts a sustained hard market. Lit fin can be a particular thorn in the side of insurers, since it affords ordinary people the opportunity to pursue insurance claims even after they’ve been denied.

Warwick explains that while investor confidence is favorable, capacity is reduced. This reduced capacity can create more difficult conditions for reinsurance programs. That’s bound to cause a spike in prices. In some areas, rates have increased as much as 75%.

While some factors were in place even before the start of the year, the uncertainty brought about by COVID has brought extreme volatility to the market. Holzberger predicts that this rate of hardening will continue to increase and intensify. Warwick predicts that rate hikes will impact territories and classes differently. He refers to one company that doubled its insurance—causing premiums to go up a shocking 1,000%.

Both experts predict good things for the future of the insurance industry. Despite some difficulties, the market is well-capitalized with solid liquidity.  

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Commercial Regulatory Issues

Georgia Senate Unanimously Approves Governor’s Litigation Funding Bill

By Harry Moran and 4 others |

As LFJ reported last week, momentum continues to build behind state-level legislative proposals that seek to impose new rules governing the use of third-party litigation funding in the U.S. 

Reporting by the AP covers a new development in the Georgia state legislature, where the Senate has unanimously passed the second part of Gov. Brian Kemp’s legislative package aimed at tort reform and third-party litigation funding. Senate Bill 69, which passed the Senate last Thursday with 52 Yea votes, amends state law to include new provisions governing the involvement of litigation funders.

SB 69 requires third-party funders register with Georgia’s Department of Banking and Finance, as well as prohibiting any foreign individuals or organisation from funding litigation in the state. The bill also sets out disclosure requirements for cases where a litigation funding agreement is present and puts in place restrictions on a funder’s ability to control the litigation process.

Senate President Pro Tem John Kennedy, a sponsor of the bill, said that SB 69  “combats the growing foreign influence” in Georgia lawsuits, and argued that the new rules contained within the bill act as a “consumer protection measure”. The Georgia Trial Lawyers Association, which opposes these attempts at reform, stated that there is “still work to be done to ensure SB 69 fairly addresses its intended purpose”. 

SB 69 will now join SB 68, the part of Gov. Kemp’s package that primarily deals with tort reform, to be debated in the House and scrutinised by a bi-partisan subcommittee convened by House Rules Committee Chairman Butch Parrish. 

The full text and status of Senate Bill 69 can be accessed on the Georgia General Assembly website.

Case Developments Commercial

LCM Announces Filing of Appeal in Australian Energy Class Action

By Harry Moran and 4 others |

As LFJ reported in December 2024, an Australian class action funded by Litigation Capital Management (LCM) had received an unfavourable ruling in the Federal Court of Australia, with the judge ruling against the claim brought over claims that two government-owned entities engaged in market manipulation to create an artificially scarce supply and raise prices.

An announcement from LCM revealed that an appeal has been filed in the class action brought on behalf of Queensland consumers against the Stanwell Corporation LTD and CS Energy LTD. The funder’s brief announcement suggested that further details around the appeal would be released in due course, stating: “We look forward to engaging further with investors after our interim results have been published on 18 March 2025.“

In LCM’s release following the December ruling, CEO Patrick Moloney had said that their “expectation has always been that an appeal in this case was likely, regardless of the initial outcome” and that they “remain confident in the strength of the underlying claim.” The previous announcement also included a top-line overview of LCM’s involvement in the case, disclosing that the funder had provided A$25m in funding from its own balance sheet capital to support the class action.

The first instance judgment from Justice Dennington in the case of Stillwater Pastoral Company Pty Ltd v Stanwell Corporation Ltd can be read here.

Commercial Special Features

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

By John Freund and 4 others |

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 Advisors.

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.