When a workers’ compensation claim is potentially at the fault of a third party, the subrogation of a claim can occur, allowing companies to minimize some of the risks associated with a claim and lower a company’s loss ratio. Whether it be a work-related injury due to a malfunction of machinery, injury at a third-party workplace, or any other situations in which a company is not directly at fault, subrogation opportunities can arise to help companies prosecute the liable party.
Subrogation opportunities, however, are far and few between due to a number of related challenges for insurers. Many companies lack experienced staff who can correctly identify subrogation opportunities, lack a proper strategy to investigate and carry out subrogation, have difficulty following statutes of limitations and regulations, and also lack the proper technology to assist in all of these functions.
Automated subrogation settlements around the world are drastically increasing, with countries such as the UK and Australia having automated settlement rates as high as 40%. However, the US automated settlement rate is between 10 and 15%, low compared to its global counterparts. The reason why the UK and Australia have considerably higher rates is because of better technology. Our Klear.ai platforms can help close the gap in automated settlements, and flag subrogation opportunities at any stage of the claims lifecycle. Our models have the ability to give key insights into subrogation opportunities that claim managers could use to appoint the correct subrogation specialist. Catching subrogation opportunities early in the claim lifecycle can help save a company time, money, and frustration.
The Klear.ai platform is powered by Artificial Intelligence and Machine Learning algorithms which are specifically designed to fit within the guidelines of the workers’ compensation and general liability industries. Our models have the ability to pinpoint subrogation opportunities earlier in the claims lifecycle to help you maximize loss recovery while reducing claims expenses. The models further prioritize and channel these claims to designated subrogation specialists within your organization. Our models work by collecting a plethora of different data inputs ranging from the injury incident, claimant demographics, employment details, medical information, and much more. These inputs are then run through our models to detect anomaly within the claim, and also ran through our business rule-driven model to automatically detect if high subrogation instances occurred.
Our models also account for region-specific recovery laws and estimates an amount that will be recovered. After a prediction and alert have been created by our systems, the information is stored and learned upon by our deep learning algorithms that build upon our base of knowledge and create more accurate models as more information is taken.
For more of how our models obtain data, we use text mining and adjuster input from the first report of injuries (FROIs) and through notes. Our text mining engine mines the textual documents, notes from adjusters, and any other documentation related to a claim to discern meaning and assign scores. Another source of information that we mine from is social media analytics. We analyze social media to find possible collusion between industry players by measuring their level of interaction with each other. These inputs are then put through our predictive modeling which is based on logistic regression, decision trees, and neural networks.
Why Klear.ai ?
The Subrogation Predictive Model designed by the Klear.ai team will allow insurance carriers to maximize loss recovery in a shorter amount of time. The Klear.ai Subrogation Predictive Model is powered by artificial intelligence and machine learning algorithms which proves to effectively and accurately predict and notify insurance professionals of any subrogation opportunities. Concerns about being able to successfully leverage Workers’ Compensation or General liability claims for subrogation opportunities can be quickly put aside with an artificial intelligence platform.
On top of lowering the loss ratio, insurance professionals can expect claim expense reduction as well. This artificial intelligence and machine learning technology is a cloud-based system that can and has been integrated with different third-party systems, making it extremely convenient. Its easy-to-use features that are customizable further increases their appeal and applicability. A demonstration of the model and the dashboard will allow potential clients to realize the efficiency and reliability of the solution. The demonstration will also elaborate more on the scoring of the probability of a subrogation opportunity, and the ways in which deep learning algorithms will collect and learn the data over time to produce even more accurate results.
What is Subrogation in Workers’ Compensation claims?
How can Klear.ai flag subrogation opportunities?
What is the Subrogation Prediction Model?
How would Klear.ai’s Subrogation Prediction Model benefit insurance professionals?
How can I schedule a demo?
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