Using big data to make smart litigation funding choices

funding-litigationEarlier this year I looked at an interesting approach that saw big data utilized to predict the chances of success for a startup.

“Industry giants like Google, Microsoft and Yahoo are spending tens of billions of dollars a year on acquiring smaller firms for market entrance, strategic intellectual property and talented employees, but face a real challenge identifying companies with the right products or technology in the vast startup universe,”the researchers say.

“Our new approach uses big data analytics and a text-mining technique called topic modeling to identify potential matches,” they continue. “By analyzing unstructured, publicly available descriptions of any startups’ business, we can quantify any two firms’ business, geographic, investor and social proximity and from there identify potential targets for mergers and acquisitions.”

Big data and the law

It’s a fascinating approach that is not confined to running the rule over startups.  A new startup, called Legalist, is using a similar approach to help finance legal action.

I’ve written previously about a number of startups that are aiming to make litigation more affordable to the average person.  For instance, Lexshares are a crowdfunding site where people can back litigation in return for a share of the payout if the action is successful.  CrowdJustice are a British site that offers a similar service.

Where Legalist differs from these is that it takes an approach not dissimilar to that mentioned at the start of this post.  They trawl through the case records to gain insight into the likelihood that a particular case might succeed, and then provide financial backing to those they regard as having a high probability.

Data backed litigation

The platform offers up to $500,000 in funding to claimants in return for as much as 50% of the settlement in the event of a successful claim.

It bases its predictions on a database of millions of past cases, with some 58 different variables used to gauge the likelihood of success in any particular case.  These include things such as the type of court all the way down to the identity of judges and even the lawyers involved.

The hope is that this data based approach gives them a more insightful approach to backing particular cases, and therefore a better chance of actually making a return on their investment, whilst also hopefully providing financial help to those whose claims deserve to be heard.

Such approaches have certainly worked in other areas, and it will be interesting to see the impact they can make in the more equitable funding of litigation.

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