Legal analytics

 

In the 2008 remake to “The Day the Earth Stood Still,” Keanu Reeves' Klaatu character says, “Your problem is not technology. The problem is you. You lack the will to change.” The legal industry has faced a similar problem. The technology and related data and analytics have been available to help both the sell side (law firms) and the buy side (clients) get better results, but the legal industry was slower to adopt technology platforms and analytics than other industry verticals.

However, over the last few years, law firms and clients have accelerated their use of technology and analytics platforms powered by artificial intelligence/machine learning and data visualization. Some of the main drivers of accelerated adoption of technology and data in dispute resolution are:

  • Law firms focused on improving their likelihood of success in a litigation;
  • Clients being more scientific in selecting outside counsel;
  • A need to do things faster and in a more cost-efficient manner;
  • A need to provide greater insights and strategic advice to external and internal clients that goes beyond a strong litigation strategy and drafting strong pleadings; and
  • A need for law firms to better differentiate themselves as a result of commoditization (there are a lot of great lawyers out there) and increased transparency into the depth of capabilities of mid-sized firms that historically were not on the radars of in-house groups.

Litigation funders are in a unique position to observe (and participate in) these trends, working with leading players across the legal ecosystem and leveraging legal technology for purposes ranging from assessing the likelihood of success in cases to business development. The following are some key trends impacting the market:

1. Evolution of E-discovery: Initially, providers of e-discovery solutions focused on platforms in which electronic data could be stored in a single place and reviewed by multiple users. Efficiency was the primary objective. However, with a dramatic increase in data and electronic communications being created by clients, functionality expanded to assist with the identification, collation, storage, review and production of electronic evidence. This includes cloud-based storage and processing, data visualization to group and relate documents to each other, and the development of machine learning and AI algorithms that assist reviewers to make relevant and privilege decisions. E-discovery software now offers functionality ranging from analytics covering issues such as reviewer accuracy, reviewer pace, and real-time reviewer activity to real-time case collaboration allowing documents to be acted on by multiple people simultaneously.

2. Cloud Computing and E-discovery: The transfer of e-discovery processing and review to the cloud has created cheap and efficient storage, easy access and scalable computer processing power. Cloud storage has significantly decreased the costs of e-discovery for clients, with storage costs within a platform decreasing by 50 percent for average matters, with costs lower than $5 available for very high volumes. The other advantage of conducting e-discovery on cloud platforms is that increasingly business data is stored in the cloud, removing friction associated with extracting data from a server, then uploading it onto another server for processing and review.

3. Analytics and Technology-Assisted Review: Another significant advance is the adoption of analytics and technology-assisted review (TAR). These tools differ from a linear review process by focusing on the most relevant documents first. Analytics leverage technology to move from Boolean keyword search and hit counts as a method of identifying relevant groups, to the use of several data points including metadata, content searching, concepts and objective information. Some platforms offer analysis of documents in a format that allows visualization of social networks, concept clouds, e-mail threading and gap analysis. TAR uses machine learning algorithms to predict the relevance of a document by applying a learning model, and then ranking the documents according to their likelihood of relevance, so that documents that are unlikely to be relevant need not be reviewed or can be reviewed using less expensive resources.

4. Legal Strategy: Another developing area of legal technology leverages legal data and analytics to provide insights into a wide range of issues which arise in the context of a dispute—for example, the likely duration of a case; how judges or arbitrators may rule on a particular issue; whether a judge or arbitrator has a tendency to rule for plaintiffs or defendants; the types of damages or remedies a court has awarded for a particular cause of action; and even the past decision making of opposing counsel in a dispute context. This impacts a number of potential decisions including, among others, choice of forum. The range of data that is now being sliced and diced is becoming progressively more novel. For example, there are services which can provide information to lawyers on how jurors might vote by searching through public records and social media posts.

5. Selecting Outside Counsel: Corporations are now using analytics to select outside counsel. In the past, it was often difficult for a company faced with a dispute in a jurisdiction in which it had limited experience, to identify the best counsel to advise them. This is no longer the case. Analytics make choosing counsel a more scientific process. Software now allows in-house teams to screen potential counsel on issues ranging from breadth and depth of experience in relevant jurisdictions, to benchmarking legal fees and expenses.

6. Preparing Arguments and Pleadings: Another significant advancement has been the use of machine learning to improve the legal research process. Various platforms use natural language processing to intelligently search case law to find the relevant precedents that were not easily discoverable on traditional legal research platforms. These platforms offer a significant advantage over traditional Boolean based searches and are able to identify that a case is relevant to an issue, even if the language in the search term is not present in the particular case. The other significant advantage offered by these platforms is data visualization, which enables users to view connections between cases and find outliers that better position them to advocate on behalf of their clients. Other platforms are leveraging AI to produce answers to pleadings that rival answers produced by junior associates.

The accelerated adoption of legal technology and analytics in litigation and arbitration is exciting. There is little doubt that the use of technology and data will continue to increase. The challenges created by rapidly growing client data sets and the lack of transparency and efficiency continue to be met by innovative ways of accessing, analyzing and presenting data. At the same time, the use of data to improve the accuracy of decision making, helps lawyers and clients to focus on key issues and be right more often.

The holy grail of true predictive analytics that can be applied in a scalable and cost-efficient manner does not yet exist, but given that all of the relevant data elements are available and more data is moving online with consistent taxonomies, readily accessible and usable predictive analytics are a game changer that is on the horizon.

 

Scott Mozarsky leads Vannin Capital's North American business. Prior to joining Vannin, Scott led Bloomberg's business across the Legal Market. In addition to his business roles, Scott practiced law for 15 years as a cross-border M&A attorney at two multinational law firms and as a General Counsel and Head of M&A and Strategy for UBM PLC.