Predicting the likelihood of a claim going to litigation is a valuable tool for your program.
The Klear.ai model provides expected probability along with other top drivers resulting in a probabilistic score which greatly improves the lifecycle management of the claim.
Klear.ai can help predicts the probability of a claim going into litigation. An early indication of litigation will help the claim managers take the appropriate approach to settle the claim.
According to a 2018 report, litigated workers’ comp claims were almost four times more expensive than non-litigated claims. As the data suggests, litigated claims are approx. 35% of claims costs in CA.
Claimants might involve attorneys soon after filing a claim or much later in the process. In either situation, attorney involvement represents a whole new phase in the claim’s journey.
With the right algorithm and the means to pull data, AI systems can identify the best attorneys for a case based on outcomes.
They can also pull and analyze key settlement data so that organizations can make the right decision about whether to settle and when. As a result, cases are resolved as quickly as possible, and claims are closed more efficiently than ever before.
Traditionally, Litigation is a major cost driver for workers’ compensation claims. The Klear.ai solution will address these costs with increased efficiency and the ability to deliver novel approaches to help companies in the United States address this area of concern.
Our technology helps to identify and detect “at-risk” claims using sophisticated machine learning algorithms and AI modelng. The insights uncovered into these claims help to mitigated their risk of becoming litigated before issues arise and resolving claims efficiently and at scale.
Why Klear.ai ?
Our product specialists can quickly guide you through our Litigation dashboards where they will break down the different parts of our models. We first evaluated basic “demo” information regarding the claim, such as the claim number, claimant name, location, injury type, etc, so that it is easy to have on hand when needed.
In the demo, you can also learn about how our Litigation Propensity score is displayed in an easy to view panel, see the top drivers of how our engine came up with this estimation, the level of confidence it has, and also any notifications of notepad sentiments or adjuster input that could lead to the severity score being affected.