Litigation analytics: what case prediction brings to the litigator’s armoury

In the film Minority Report, Tom Cruise, as the head of a “pre-crime” unit, apprehends criminals based on foreknowledge of their crime, provided by psychics. This results in low crime levels, but also in a world where knowing the future limits choice (and implicitly freedom) and access to justice.  

The reality of case prediction practice is very different from this dystopian vision. 

Today, the goal of predictive analytics for civil litigation is to combine rigorous data and statistical discipline with the expertise of the litigation practitioner. It is not to replace the role of the litigator with a robot lawyer that publishes the answer for the client. The result is improved prediction, advice and outcomes for the litigator and their client.  

This is key. Litigation lawyers already predict outcomes, almost always at the request of their client - not just on the question of their chances of success but also as to how long it will take, how much it will cost and what impact a particular judge, barrister or witness will have on the outcome.  

 The objective of case prediction analytics is to enhance this, not replace it.  

Using rigorous statistical models and accurate, extensive and relevant data, it is possible to produce well-informed baseline predictions which practitioners can tailor and refine before turning into client advice. Extensive research demonstrates that working this way delivers dramatically improved forecasts of outcome and, consequently, better decisions. In Superforecasting: The Art and Science of Prediction, Philip Tetlock and Dan Gardner show how the use of robust baseline data and statistics, coupled with self-critical human analysis, achieved predictions 60% more accurate than control groups.

How does it work? 

Technically, predictive analytics comprises three components: statistical/data analysis, modelling and human-led review and adjustment. On the statistical side, data scientists take multiple data points  across as many variables as possible. They then analyse the data variables to uncover patterns in historic results which correlate with an outcome. These patterns and variables are then modelled to generate a prediction which can be tested against previous and future cases with matching variables.  In litigation these are often relatively obvious. For example, the nature of the claim (contract or tort), reliance on factual evidence or points of law, remedy sought, arguments deployed, and the judge and barristers involved can all have a statistical influence on the outcome.   

In summary, the degree and relationship between multiple variables determine the predictive outcome. Is the identity of the judge more influential than the reliance on expert evidence, for instance? It may also be dependent on one of many other variables. This complex combination of variables is essentially the formula that is applied to forecast future outcomes. In reality though, this is relatively complex mathematics which non-experts have little chance of being able to explain to a client. Few clients will invest substantial funds in a piece of litigation based on a mathematical model they don’t understand, forecasting a future event that may not eventuate for some time, if at all. 

How then does this help practitioners? 

In current litigation practice, the lawyer will tend to use the historic pattern of results as the basis for beginning to build their own forecast, which they can use to guide client advice and manage their expectations. If you imagine that all cases fall within a spectrum of predictive success starting with <1 percent (almost no chance) to >99 percent (almost certain), cases that sit at the top and the bottom ends tend to be the easiest to predict. As you move to the middle it becomes harder to forecast with a high level of confidence. A claim with a 40 percent chance may well succeed, even though it will, on the basis of probability, fail 6 times out of 10. Determining that outcome is very difficult but knowing that it is 40 percent as opposed to 60 percent is meaningful. 

For the practitioner, recognising that a claim has a 40 percent chance of success (based on past cases with similar features) gives them a starting point for their own prediction. Does the claim require the judge to reject a long-established precedent? By how many percentage points would that alter the baseline? Does the reliability of the factual witnesses move it closer to 50 percent? Is a commercial interpretation of a contract key to success, therefore taking it down a few percent? 

The more carefully the practitioner undertakes this exercise, the more accurate their forecast becomes, particularly if they self-critically challenge their prediction and assumption for each component of the case 

At the end of this process, the litigation lawyer has the basis for sound advice to the client. (Of course, they will also gauge the client’s ability to pay, appetite for risk and motivation to stay the course.) 

With rigorous data and analytics used in this way, a lawyer’s ability to accurately forecast and then effectively guide and advise a client is meaningfully increased. This is especially so where the client is data-led in their approach. For example, it may help to persuade a CFO to pursue expensive litigation if you can supply data they can utilise in their own financial and risk models.

Strategic advantage 

The value of predictive data and analytics does not end there. For many cases, the process of determining strategy and argument relies on a combination of litigator experience and case law research. Litigation analytics can help the user find the best arguments, tactics and insight, by focusing on the factors that impact outcome.  

Summary judgment application in front of Walker J? The data tell you that just over 69 percent of such applications fail in front of Walker J, as opposed to just 31.5 percent on average for the Commercial Court as a whole. This data point is key; it may well  change your mind about making the application. But of as much value is to be able to readily access the relevant judgments , to  understand why the applications failed.  

The analytics can also give an edge or anchor point for settlement negotiation. If 66 percent of negligence claims fail at trial, the defending party introducing that data point could put considerable pressure on the claimant to raise doubts as to the prospects of their case at trial and encourage settlement. The analytics can also potentially form the basis or starting point for agreeing the settlement value of a claim.  

Industry growth 

As a decision-support offering, litigation analytics is  widely used in the USA. A recent FT article suggested that around 75 percent of the top 100 US firms already make use of litigation analytics services. There are broadly two main provider groups: the large legal research providers including Bloomberg, Thomson Reuters and LexisNexis (Lex Machina) who have provided analytics add-ons to their core offerings; and a range of agile litigation analytics businesses offering new solutions for US jurisdictions, with companies like Premonition, Gavelitics and Ross Intelligence offering algorithm-powered litigation support. 

A key stimulus of this growth is the increasingly data-led decision making that large corporates and their legal functions use in order to drive more aligned business plans and decisions. This has created a demand for data to support business and risk management decision-making where the executive leadership can balance the risks (financial, regulatory and reputational) with the rewards that success would deliver. 

Unsurprisingly, this same driver for more data-oriented legal decision-making in commercial organisations is generating increased interest in the UK. 

Accessibility of data 

While the demand in the UK is growing, and we have seen considerable interest ourselves, the availability, consistency and usability of case data is substantially lower than for US courts. The UK courts system is not set up to enable fast and straightforward production, publication and distribution of court information, including judgments and other court documents.  

In addition, this variability in the availability and accessibility of data makes the use of advanced machine learning technologies more difficult. The lack of consistent templates, styles and content structure makes it harder for these tools to work and learn as quickly. 

The incorporation of litigation analytics data into the key litigation decision-making and research processes will become widespread as clients seek support for their data-led decision making and litigation lawyers look for every area of advantage and a competitive edge. The ability to find relevant data more quickly will also help to even out some of the resourcing imbalances where smaller or less well-resourced legal teams go up against the big firms. 

New skills for litigators 

As in other areas, while data brings value and insight, relying on it is not without risk. The first risk is the misinterpretation of data and analysis . Lawyers need to master key areas of data and statistical analysis in order not to fall into this trap. The smarter litigation analytics firms are supporting their clients with training and providing helpful gradings, scores and guides to assist with this. Importantly, this investment in new skills and competencies will have a benefit in improving the forecasting accuracy of those that engage with it.  

The second risk lies in the tendency to shortcut decisions by assuming the data provides the full answer. Once the data suggests a claim has a 70 percent probability of success, that chance can become a fait accompli. Without careful management, we risk short-changing  potential litigants and removing their access to justice. It also means that potential cases on which the law may well need to be  explored may never be brought to court because the likelihood of success is too low. 

It will require the thoughtful application of focused insight to ensure this doesn’t happen. Automated tools spitting out answers provide little real value. 

This is why Solomonic’s focus is on marrying the analysis and insight the data can reveal with the skills, instincts and talents of the litigator. Practised this way, these risks are not only manageable but effectively identified in advance. As a result, the art of litigation is enriched and this adds to the quality of decision making and advice that  litigators can provide to their clients. 

As a way of enhancing the skills and abilities of  litigators and the value of the service they provide to clients, litigation analytics has a very promising future. 

Author: Edward Bird, CEO, Solomonic