What Insurers Need to Know about Litigation Analytics
by James Chapman
Litigation analytics is the cutting-edge way to use data and analytics to reduce uncertainty in lawsuits, win more cases and settle the ones that can’t be won.
It is a relatively new field that is growing in popularity as more claims departments realize the benefits of using data to inform their litigation strategies and improve their defense of claims.
As claims professionals know, litigation is an area where there is a lot of uncertainty, and the stakes are often high.
With the advent of litigation analytics, data-driven predictions of case outcomes are now possible. Analytics can also be used to help develop litigation strategies, predict legal costs, and identify the best attorneys to handle specific cases. While analytics is not likely to replace human judgment, these tools can be used to complement and speed up traditional research and evaluation methods.
Litigation analytics has evolved recently to include artificial intelligence and machine learning.
One of the key advantages of using analytics in litigation is to identify patterns and trends that would be difficult to spot otherwise. Machine learning techniques can quickly identify relevant patterns from diverse data sources to make better decisions because it can learn from data.
For example, data can be used to identify which cases are most likely to result in a favorable outcome. Multiple factors can simultaneously be considered, such as data about the venue, the judge, the attorneys, case durations, injury severity, and even settlement negotiations. This information can then be used by insurers to focus on cases with the highest likelihood of success and settle cases that would be hard to win.
Litigation analytics can also be helpful in resource allocation.
By analyzing data from past and current litigation within the claim department, variables that will drive up costs in a hypothetical portfolio of future litigation can be identified. Carriers can then make more informed budgeting and staffing decisions.
There are many sources of data that are useful for litigation analytics.
One obvious data source is court dockets. All federal courts and some state courts have public on-line dockets, allowing for the harvesting and normalizing of court data. At scale, this data provides valuable insights into how similar cases have been decided, how long they took, differences among judges, sanctions for misconduct, and the like. They can also identify arguments that tend to be successful (or unsuccessful) with a particular judge.
Another rich source of data for litigation analytics is public records. This includes things like property records, voting records, criminal records, and more. This information can be used to help build profiles of potential jurors or witnesses, understand the background of a case’s participants, and predict how a case might play out based on cases with similar profiles in the past.
A third source is the carrier’s own data, including reserves, settlements, legal and expert fees, and the strategies pursued in cases. This type of data can inform the patterns around motions practice success, cycle time, win-loss metrics and help identify rising star defense attorneys.
However, there are also some potential problems with using litigation analytics.
One is that not all data is created equal. Some data may be more reliable than others, and some data may be more accurate than others. This means it is possible for the results of a litigation analysis to be misleading or inaccurate. The saying “garbage in, garbage out” applies here. A skilled data science team that includes legal subject matter experts is essential to avoiding this problem.
Another potential problem is that, even if the data is accurate, it may not be representative of the entire population of cases.
For example, while U.S. federal court records have been available online for two decades, state courts have been slower to adopt consistent electronic case filing practices. This means that there could be important factors that are not being considered in the analysis. Fortunately, several vendors are rapidly developing and normalizing state court data all across the country to fill these gaps.
But it’s a bigger problem not to get started.
We find that carriers often feel they need to spend a lot of time and money on big data projects — such as installing a new enterprise system or building an extensive data warehouse – before they will be able to effectively use analytics.
Waiting until the “data house” is in order to start using advanced analytics can mean waiting years, never extracting value from the data that is already available because it is always under construction. Once ROI is proved with even limited data involving a small group of stakeholders, that justifies scaling up and investing more resources in broader analytics initiatives.
Overall, litigation analytics is a powerful tool that can provide insights into every aspect of the litigation process. As data becomes more readily available and easier to analyze, it is likely that more carriers will turn to this approach to gain an edge, both in the courtroom and in settlement negotiations.