Legal technology innovator McCarthyFinch was on the TechCrunch Disrupt SF Battlefield yesterday (5 September), one of the 20 chosen companies out of a thousand to compete to be recognised as the world’s best early-stage startup. McCarthyFinch showed off its virtual lawyer that it says can be trained and plugged into any legal process, from financial compliance to contract automation. Oh yes, and it has also just announced new partnerships with Accenture and Deloitte.
McCarthyFinch started out as a project at MinterEllisonRuddWatts in New Zealand, which looked at leveraging tech to automate legal processes including legal document review and discovery. It uses natural language processing to interpret documents and claims a 90% accuracy rate.
“Our AI platform learns up to 100 times faster than traditional AI, using far smaller data sets,” said Nick Whitehouse (pictured), CEO and co-founder of McCarthyFinch. “Most importantly, its results are entirely explainable, defensible and trustworthy.”
To help expedite the rollout of its technology, McCarthyFinch yesterday announced new partnerships with Accenture and Deloitte. Deloitte will integrate McCarthyFinch services into its own technology. Accenture is engaging with McCarthyFinch on a number of government projects beginning in Australasia. According to TechCrunch, McCarthyFinch doesn’t intend to sell its platform directly to law firms but as a service to other software vendors, to save them having to build their own product from scratch.
Some of the legal processes the platform called Author can be trained and plugged into include:
Settlement Insight: Helps legal teams negotiate the best settlement for their clients or companies by extracting settlement values from 35,000 agreements across the U.S. It then groups these values by court, state and other parameters to create comparable databases.
Contract Favorability Review: Breaks down a contract into different clauses and sections, analyzing each part and identifying whether it’s in a client’s favour or not. It also identifies missing or added clauses and text to speed up everyday document review.
M&A Insight: Reads through public and private mergers and acquisitions to create an insight report for advising clients, negotiating transactions and attracting new clients. At one firm, this reduced junior legal effort by 99 percent.
Court Decision Classification: The AI platform was put to the test classifying court decisions against a team of legaltech workers. While it took the human team six weeks and an undisclosed cost to classify five decisions with 91 percent accuracy, it took the AI platform only two hours and no cost to classify eight decisions with 94 percent accuracy.
Tax Determination: Revolutionizes legal research for government tax decisions, turning a multi-week process into one that only takes minutes. Author does this by taking latent information from physical documents and turning it into a research interface.
Transactional Contract Automation: Author automatically approves contracts when they conform to existing laws and company policies, providing review tools and insights for the in-house teams only when the contract needs a second look. This reduces in-house legal workload, ultimately allowing revenue to be brought in faster.
Legal Triage: Author determines the value of clients by acting as a web interface for public legal questions. In an experiment where Author fielded questions before passing them along to lawyers, time spent to resolve these questions reduced by 43 percent. The firm helped twice as many clients for the same cost and effort while resolving low-value questions with little interaction.
Financial Advice Compliance: Author highlights where inappropriate or incomplete financial advice has been given by comparing the goals of a client with the financial products they’ve been sold.
We’ve yet to have a demo or drill down into Author’s capability in detail. All comments welcome.