As litigation finance spreads to more jurisdictions and becomes increasingly accepted as an appropriate way to bankroll lawsuits by litigants, lawyers and the courts, players in the business are looking at the best way of finding cases with a good chance of success.
Now, attention is turning to artificial intelligence as a way of spotting and analyzing cases that look as if they’ll be successful. Picking winners is clearly important to litigation finance firms and their investors, who are betting large sums with the expectation of sharing a percentage of the proceeds or a multiple of their invested cash, provided the case is decided in their litigant’s favour in court or gets settled.
CourtQuant, a UK startup, claims its software can predict legal case outcomes and can provide a series of tools to help with labour-intensive legal and insurance cases.
“Almost everyone I know is afraid of going to court,” says Ludwig Bull, the 23-year-old founder of CourtQuant, which he set up with a partner after graduating in law from Cambridge University. “It’s because trials are unpredictable, and litigation risk is something very difficult to understand.”
He claims that AI is actually better at predicting outcomes than legal experts and his software can forecast the likelihood of a settlement, provide “win rates” for specific lawyers and give timelines for cases. He claims accuracy of up to 90 per cent.
Bull said in an interview that his main clients are litigation funders and insurance firms, although he’s confident of attracting large law firms as well. The firm, which still is in its infancy, is based in the UK but is expanding into Europe, the U.S., and Canada, where he says there has been interest from litigation funders.
Bull gives an example of a breach of contract lawsuit where the litigant is suing for $10,000. He says his software can predict the chances of winning in court and by analyzing all the factors involved, come up with a specific estimate of how much money the plaintiff is likely to win.
“If you’ve got a value for the case, that helps you (as a litigant), and that also helps funders and insurance companies to assess the risks properly,” Bull said.
While the sales pitch sounds good, the jury is still out as to the effectiveness of AI and whether it will be useful in complex cases, and in smaller jurisdictions like Canada. And critics note that available data usually exclude the vast majority of cases that end in out-of-court settlements.
“AI systems are beautiful at spotting patterns,” said Richard Tromans, a London-based consultant to legal firms. “They can see a pattern in a very big complex set of data that a human can probably never see or it could take days or weeks for a human to find.”
AI is probably most useful in employment law, accidents or similar kinds of cases where there’s lots of data available, he says. “If a machine can read 5,000 cases that are very similar and tell you in 10 minutes what it would take a month to do (manually), you’d be crazy not to use it.”
Ben Alarie, founder and CEO of Toronto’s Blue J Legal has pioneered the use of AI in tax and employment law. The firm, which has recently expanded to New York and Washington, D.C. has sold its products to 12 of Canada’s 15 largest law firms and nine of the country’s 10 largest accounting firms as well as to the federal government itself, he says.
“In the long run, these forms of algorithmic analysis will make their way into every area of law, and we expect to be part of it. But we’re starting with the areas where people crave clarity and certainty,” says Alarie, who also teaches law at the University of Toronto.
When a user logs into Blue J Legal’s Tax Foresight software, it asks a series of questions about the client’s situation and the salient facts in the case. The programme will then make a prediction based on the case law and will project how a court would decide the outcome.
“Then, they’re in a very strong position to be able to advise their client on their prospect of success,“ Alarie says.
So far, litigation finance firms haven’t purchased Blue J’s services, but Alarie says it’s only a matter of time.
When it comes to tax cases, Blue J Legal can tap into the digitized database that captures all proceedings and rulings involving the Tax Court of Canada, the Federal Court of Appeal and the Supreme Court. The problem is that the same kind of data isn’t available in all Canadian jurisdictions. Ontario courts, in particular, are a hit or miss affair, with not all cases available in digital format.
Nicolas Vermeys, a law professor at the University of Montreal’s Law School and an expert in cyber-justice, is skeptical about whether AI can be as useful if the volume of cases is low. “AI is good when there’s volume. If there are only a few cases, you’re probably better dealing with them manually.”
He wonders whether Canada, with a population of 37 million and two legal systems, can produce the volume of cases where AI would be useful at this point in time.
Ezra Siller, managing director of Nomos Capital Corp., a Toronto litigation finance firm, agreed that there are currently "systemic constraints" in Canada that hinder the possible use of AI in the evaluation of investment opportunities. But that could change. "It's possible that eventually AI could be one tool among many in our intake or diligence processes," he said.
Paul Rand, Chief Investment Officer for Canada at IMF Bentham Ltd., one of the world’s leading litigation finance firms, also points to U.S. jurisdictions, where court records are broadly available in digital form, which is not the case in Canada. “That presents a quantifiable difference in the ability of applying AI,” he says.
Since it opened a Toronto office in 2016, IMF Bentham has vetted 400 applications for funding and so far tapped 14 cases for investment. “We’re not a volume business,” says Rand.
Bentham doesn’t fund personal injury, discrimination, or malpractice cases, and so far, the main source of cases has been from law firms. But increasingly, says Rand, litigants have been contacting it directly.
“We’re interested in what the future holds (for AI), but at the same time, our experience is that it’s a lot of possibility, but not something that is easy to apply at this stage,” he says. “At the moment, we rely pretty heavily on the actual intelligence of lawyers.”