AI In Claims Litigation

By April 10, 2019 August 28th, 2019 Blog
Claims Litigation and AI

A claim litigation process is started when a claimant or third-party insurer is not satisfied with the claim decision or the payment offer. Insurers have to find ways to defend themselves against the lawsuits from their policyholders and third party.

The general steps involved in handling litigated claims are:

Claims Litigation and AI

The exhaustive nature of claims litigation process makes it very time consuming and costly. Even though very few claims go to litigation, the claims settling cost reported in insurers’ financial statements as “Defense and Cost Containment Expenses (DCCE)” are quite high. The following figure shows various costs that sum up as DCCE.

Claims Litigation and AI

Claims Litigation and AI
The graph shows DCCE expenses for five consecutive years from 2013-2017, depicting a growing trend. Defense and Cost Containment Payments incurred only for private passenger auto liability/medical by insurers as a percentage of total payments made in the year 2017 is 4.21% as per the data provided by The National Association of Insurance Commissioners (NAIC).

Costs of defending certain types of lawsuits, such as medical injury cases and class actions against pharmaceutical companies are relatively high. This is reflected in the annual financial statement of the insurance market released by the Insurance Information Institute (III). As per the financial statement in 2017, in addition to $940 million incurred in product liability losses, insurers spent $648 million on settlement expenses, equivalent to 68.9% of the losses.2

The primary reason for losses incurred by insurers as DCCE is the difficulty in predicting a lawsuit in the initial stages of the claims process. Hence, the claims representative has to thoroughly scan through all aspects of the claim details relating to the incident, plan & process each claim in good faith, to ensure optimum payouts and avoid future re-opening of these claims due to potential litigations.

AI can be used to, not only, predict the probability of litigation, in the early stages of a claim, but also provide inputs to enable decision making & take necessary steps for faster & optimum resolution of claims. AI has the advantage of using large amount of diverse data sources to obtain unique information for segmentation and analysis of claims. This helps to identify the combination of complex patterns that point to a potential risk of litigation.

Employing AI, we can assess each claim & provide below predictions, in the early stages of a claim life cycle, to ensure better outcomes:

  • Probability of litigation in a claim
  • Key team members for successful outcome of litigation
  • Optimum payout offer for quick & satisfactory settlement
  • Optimum time to resolve a claim

The involvement of the right teams from the early stages of a potential claim can result in a reasonable compromise between the claimant/third party and the insurer or better preparedness to face the litigation process. Artificial intelligence algorithms are also capable of mining Big Data to find out which attorneys win which types of cases and before which judges. Hence appointing the right attorney in the right case can increase the chances of winning the litigation. AI algorithms can also be employed in obtaining invaluable insight on optimized settlements for specific historic cases through exhaustive analysis of historic claims. The high computational power associated with AI provides instant access to quality and quantity of information about similar claims; this can help take the decision on whether to fight or settle a pending lawsuit. This results in substantial savings in time and money for the insurer.


With the integration of AI in the claim life cycle, the risks associated with a claim going for litigation can be predicted effectively. With this as input, and involvement of the most appropriate team members suggested by the AI algorithm, the defending or settling of a claim by compromise becomes easily achievable, potentially impacting the customer experience, adjustment expenses and losses for the insurance carrier.


Sheetal Kumar

Sheetal Kumar

Sheetal Kumar is a Technical Architect at InsurAnalytics. Read More Posts

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