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The current (and future) state of AI in legal finance

February 22, 2024
David Perla
Perla 2021 12 Burford Capital Group #2 1034 B&W Sm


In an article originally published in New York Law Journal, Burford Co-COO David Perla examines how AI is being used and could potentially be used in legal finance.

AI in law has garnered significant attention, but its capabilities may not live up to the hype. As a lawyer and legal entrepreneur whose career has coincided with multiple phases of transformation in legal technology, I have learned to approach each new innovation with caution. From the introduction of email to the advent of the internet and e-discovery, there has always been a period of initial excitement around new technologies. However, it is crucial to distinguish between practical and achievable solutions and lofty promises and ideas not yet ready for implementation.

In this article, I examine how AI is being used and can potentially be used in legal finance—the practice of providing financial resources and funding for litigation and arbitration claims. Specifically, I will examine the role of AI in sourcing investments, making investment decisions and predicting outcomes once investments are established.

Originating new business and identifying potential cases

As in other industries, AI can enhance new business efforts. In legal finance, providers leverage technology to enhance the process of identifying potential cases for investment. This use not only benefits funders but can also help prospective users of legal finance effectively identify meritorious claims for harm they’ve suffered that have the potential to generate significant value for their businesses that might otherwise be overlooked.

One application of AI in new business is combining public data with internal insights gained from previous successful investments. In doing so, legal finance providers can use heuristics and prompting techniques to identify lawyers, cases that align with their investment parameters and even to uncover harm suffered where an aggrieved party has a strong claim of which it is unaware.

To illustrate, Burford undertook a "skunkworks" project aimed at identifying lawyers who might be interested in funding opportunities based on their prior involvement in certain types of cases. This initiative involved scraping the web to identify lawyers who fit specific profiles and adding them to Burford's database for potential outreach. By analyzing the data and patterns from these lawyers' past work, legal finance providers can identify lawyers who represent clients likely to have meritorious claims that value legal finance as a supportive solution.


Legal finance is typically provided on a non-recourse basis, meaning that providers only receive a return if the case being financed resolves successfully. Thus, funders must carefully assess the merits and viability of a case before providing funding. They conduct a rigorous diligence process to evaluate the strength of the merits, damages claims, duration and the economics of the potential financing arrangement. This careful assessment helps ensure that funders invest in cases with a high probability of success, minimizing the risk of funding frivolous or unmeritorious claims.

Advanced technology, including algorithms, machine learning and AI have the potential to enhance the underwriting diligence process in the legal finance industry. Leveraging these technologies is often seen as the "Holy Grail" for the industry, promising improved efficiency and accuracy in investment decisions.

While Burford has made strides in experimenting with AI using closed case files, it is important to note that the current capabilities of the technology are limited. AI is currently most effective in early-stage issue-spotting and identifying and assessing elements of the underlying cause of action. However, it lacks the experiential data set required to make investment decisions.

The underwriting process in legal finance involves complex assessments of risk, evaluating subjective variables and considering idiosyncratic risks. These aspects require specialized expertise, human judgment and extensive experience that AI currently cannot replicate. While AI can assist in certain aspects of the underwriting process, it is not yet capable of making investment decisions independently, even with extensive prompting.

Human expertise is crucial because it adds an extra layer of insight based on accumulated experience and data. The ability to assess the nuances of a case, understand the significance of certain factors and make informed investment decisions is a skill that human experts bring to the table. Clients of all kinds routinely tell us how much they value the human insights and judgment provided by legal financiers whose experience is based on many decades of complex litigation experience and exposure to billions of dollars of commercial litigation and arbitration matters.

Improving internal modeling capabilities

In the legal field, a significant challenge arises from the lack of widely available data on commercial disputes. Models are only as good as the data fed into them. Approximately 90% of commercial disputes are settled, meaning that 90% of the data about commercial disputes (e.g., the terms of the resolution) remain confidential to the involved parties, including litigants, legal finance providers and insurers. The lack of data means that very few businesses and law firms have developed the expertise to effectively evaluate litigation economics. Attempting to build a predictive model for commercial disputes based solely on publicly available data would inevitably fail because in the best case scenario it would be looking at only about 10% of the data, and none of that data relates to settlement value, arguably the single most important piece of information.

To effectively model the outcome or value of a litigation or arbitration, a reliable framework and sufficient data are essential. As the world’s largest provider of legal finance, Burford Capital has reviewed hundreds of billions of dollars' worth of commercial disputes over its almost 15 years, and that unique data set has been used to construct a model that is applied to potential and ongoing legal finance matters. Burford's strategy and quantitative analytics team collaborates with in-house legal underwriters, combining proprietary data, a robust analytical framework and AI-powered enhancements to evaluate and determine pricing for cases.

Burford employs an internal model for each investment that comprises coded and tagged nodes that interact with one another to establish a comprehensive relational understanding of the investment. This model allows Burford to analyze various factors and assess—among other factors—the potential profitability, duration, potential settlement value and likelihood and quantum of partial loss associated with each investment opportunity. Clients benefit from Burford’s modeling expertise through insights and analysis that can help to inform their litigation decision-making process.

To further enhance the capabilities of their models, legal finance providers can actively incorporate advanced technologies, including AI. By integrating AI into existing models, legal finance providers could improve the accuracy and speed of their assessments, enabling them to make more informed investment decisions. AI algorithms can analyze large amounts of data, identify patterns and extract valuable insights that may not be readily apparent to human analysts.

But as experts universally acknowledge, the quality of AI models is heavily reliant on the quality of the data they are built upon. If models are constructed using flawed or limited data, the predictions they generate will also be flawed and limited. As noted, there is a dearth of public data on commercial disputes—and that means robust AI-powered modeling tools based solely on public data are a long way off.

AI has potential, but many limitations

While AI is not yet capable of making investment decisions, it shows promise in enhancing early-stage assessments, originating cases and improving internal modeling. As AI continues to evolve, it may play a significant role in shaping the future of the legal finance industry, but at least for now, human expertise is still the main ingredient for a successful legal finance company.

Reprinted with permission from the February 02 edition of the New York Law Journal © 2024 ALM Global Properties, LLC. All rights reserved. Further duplication without permission is prohibited, contact 877-256-2472 or [email protected].