Continuation Event Risk Scoring for COBRA Coverage Affordability
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Solution Overview
Problem
Employees facing involuntary job loss often struggle with the high cost and affordability of continuing their health insurance coverage through COBRA, as they must bear the full premium and administrative fees, making it difficult to access favorable coverage options after job termination.
Innovation Solution
An automated risk assessment framework using machine learning models analyzes various data sources to predict the likelihood of continuation events, such as layoffs, and determines risk scores for continuation events, enabling personalized insurance products to cover the premium difference or provide coverage, thereby making COBRA more affordable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If employees continue health insurance coverage through COBRA after job termination, then coverage continuity is maintained, but the financial burden increases significantly as employees must bear full premiums and administrative fees
Solution Approach 1:
The patent introduces an intermediary insurance product that mediates between the employee's need for continuous coverage and the high cost of COBRA. This intermediary product covers the premium difference between COBRA and employer-sponsored insurance, making COBRA more affordable while maintaining coverage continuity. The intermediary acts as a financial bridge that resolves the contradiction between reliable coverage and manageable costs.
Solution Approach 2:
The patent changes the financial parameter of COBRA coverage by introducing a subsidized model where the cost structure is modified. Instead of employees bearing the full premium and administrative fees, the subsidy covers a portion of these costs, effectively changing the affordability parameter while maintaining the same coverage continuity benefit.
2Measurement precision
If automated risk assessment frameworks use machine learning models to predict continuation events, then accurate risk scores are generated for personalized insurance products, but data processing complexity increases
Solution Approach 1:
The patent implements self-service through automated machine learning models that independently process data and generate risk assessments without requiring manual underwriting intervention. The system automatically searches multiple data sources, analyzes the information, and produces risk scores, enabling the insurance product to serve itself in the risk assessment process while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical manual underwriting process with automated machine learning algorithms. Instead of human analysts manually reviewing data and calculating risk, the system uses computational models to automatically process data from multiple sources and generate risk scores, reducing processing complexity while improving consistency and accuracy.
Data Source
AI summary
Concepts related to automatically assessing risk associated with continuation events are described. In one embodiment, a computing device includes a memory device to store computer-readable instructions thereon. The computing device further includes at least one processing device configured to execute a search of at least one digital platform having data associated with an employee of an organization via a computer network. The at least one processing device is further configured to identify data indicative of a potential continuation event associated with at least one of the organization or the employee. The at least one processing device is further configured to determine, using a machine learning model and based at least in part on the data, a risk score for the potential continuation event occurring within a time frame.