The role of NLP and AI in third-party litigation funding: How insurers can leverage data analytics to level the playing field

Third party litigation funding (TPLF) has emerged as a powerful tool for plaintiffs seeking financial assistance to pursue legal action. TPLF is the process by which a third party provides funding for a lawsuit in exchange for a share of the settlement or damages awarded. In recent years, TPLF has become more popular, and its use has increased in various jurisdictions globally. In the U.S., the TPLF market has reached $2.8 billion in 2021¹.

Uses of NLP and AI in TPLF

The role of data and analytics in TPLF is significant. The third-party funders assess the risks of each lawsuit, relying on robust data sets and AI tools to evaluate the likelihood of a successful outcome. The use of data analytics enables third-party funders to assess the strengths and weaknesses of a case, predict the probability of success, and determine the potential settlement value.

Third-party funders rely on analytics and AI in various ways, such as:

• Risk assessment: Third-party funders use data analytics to assess the risk associated with a particular case. They analyze various factors such as the legal team's track record, the strength of the evidence, and the likelihood of a successful outcome. By utilizing data and analytics, third-party funders can make informed decisions about which cases to fund, and at what level.
• Case valuation: AI also helps in determining the potential value of a case. Third-party funders evaluate the potential settlement value of a case based on historical data from similar cases, legal precedent, and other factors. By leveraging policy limit search, third-party funders can identify the insured's policy limits, providing valuable insight into the maximum amount that may be recovered from the defendant's insurer. This allows them to make more informed decisions when assessing the case's potential value and determining whether to invest in the litigation.
• Portfolio management: Third-party funders use data and analytics to manage their portfolio of cases effectively. They can track the progress of each case and adjust their funding levels as needed, identifying trends and patterns to optimize their funding decisions.

Benefits of NLP

TPLF presents a significant challenge for insurers, who must adopt AI to effectively counter its impact. With third-party funders investing heavily in analytics, insurers must deploy their own AI to level the playing field. However, insurers have an untapped resource in their unstructured data. By utilizing Natural Language Processing (NLP), insurers can leverage unstructured data, such as adjuster notes, to inform their predictive models and gain a competitive advantage. By investing in AI, insurers can:

• Predict outcomes: Insurers can use predictive analytics to estimate the outcome of a case based on historical data from similar cases. This allows insurers make well-informed decisions about settlement offers and other strategic decisions.
• Identify top defense firms: Insurers can leverage data analytics to pinpoint the most effective defense firms. By retaining high-performing attorneys, insurers can increase their chances of achieving favorable outcomes in litigation.
• Manage claims: Insurers can use data and analytics to manage their claims more effectively. They can identify trends and patterns across their claims portfolio to optimize their claims handling processes.

Conclusion

In conclusion, data and analytics play a key role in TPLF. By investing in AI, insurers can level the playing field and make informed decisions about claims handling and litigation strategies. In a rapidly evolving legal landscape, the role of AI will be essential.

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¹ https://news.bloomberglaw.com/business-and-practice/big-law-warms-up-to-litigation-finance-as-deals-pot-hits-2-8b.