Our proprietary models give targeted reserve recommendations for every claim based on a survey of historical data including claimant demographics, injury specifics, procedures, prescriptions and other compounding factors like comorbidity.
Klear.ai’s proprietary models give targeted reserve recommendations for every claim.
Setting reserves for workers’ comp claims has never been a simple task. We have developed sophisticated AI models and other predictive approaches to worker’s compensation reserve setting that makes the job of claim adjusters, risk managers significantly easier in view of new Covid-19 compliance requirements.
Our recommendations are based on both a survey of historical data (including claimant demographics, injury specifics, procedures, prescriptions, and other compounding factors like comorbidity) as well as future-focused modeling for accuracy and effectiveness.
Traditionally, setting reserves for workers’ comp claims has always been a tedious and complicated process. Affected by so many parameters, reserving workers’ compensation claims has become the ultimate task for the modern Claim Adjuster.
Klear.ai’s Reserve modeling evaluates many dozens of variables and with the combined output of 8 separate models, we are able to derive accurate, predictive insights into even the most complex, of regulatory jurisdictions. ( California ).
Typically, the target reserve is broken down into the same categories an adjuster would use to calculate a reserve (litigation, medical, and disability costs).
Normally, the reserve is set by knowing data components such as the AWE (Average Weekly Earnings) and the severity of the disability of the injured party. For example, if the worker is temporarily injured with a partial disability, unemployment compensation benefits can be deducted from money put into the reserve. On the other hand, if the injured worker has a permanent total disability, more costly benefits such as a life pension may be put into the reserve which will obviously increase the amount of money put in. Klear.AI uses a very sophisticated algorithm covering over 25 or more key variables (including Adjuster Notes) to make accurate reserve predictions.
Why Klear.ai ?
A demonstration of our reserve modeling and prediction capabilities is the very best way to understand the sophistication and user-friendly environment of our technology/platform.
Klear.ai is a pioneer in the workers’ compensation Artificial Intelligence / Machine Learning algorithms used to predict claim scores including reserves, fraud, litigation and subrogation.
Klear.ai has a proven track record of reserve models that are able to predict at an average of 85% or more accuracy - as compared to experienced adjusters.
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Is Klear.ai able to recognize claims that are "stair-stepped"?
The most common type of reserve setting failure is the stair-stepping claim in which the reserve keeps getting adjusted over a long period of time with no end in sight.
How accurate is Klear.ai when predicting reserves?
How can I schedule a demo?
You can book a free comprehensive demo here