Cost of Waiting Module for Workers Compensation Claims
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Workers' compensation insurance carriers face challenges in accurately predicting future liabilities for workers' compensation claims, leading to reserve amounts that frequently exceed or underestimate actual costs, resulting in significant financial losses and operational disruptions.
Innovation Solution
A computer-implemented system and method that includes a Cost of Waiting (COW) calculating module and a machine learning module to determine whether to settle claims based on an adversely-developing predictor, using seasoned WC claim data to identify which claims are likely to develop adversely, thereby targeting open WC claims for settlement and refining reserve funding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual claim assessment methods are used, then claims processors can evaluate claims using professional judgment, but the system lacks objective data-driven predictors leading to inaccurate reserve predictions
Solution Approach 1:
The patent segments the claim assessment process into distinct functional modules: a data collection module that gathers historical claim data, a machine learning module that processes the data to generate predictors, and an assessment module that applies the predictors to individual claims. This segmentation allows each module to specialize in specific tasks, improving overall prediction accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent introduces machine learning-generated predictors as an intermediary between historical claim data and final reserve predictions. These predictors act as mediators that translate complex historical patterns into actionable assessment criteria, enabling claims processors to make more accurate predictions without directly analyzing overwhelming amounts of raw data themselves.
2Reliability
If case reserves are set by the Claims department using traditional methods, then the process maintains operational simplicity, but reserve amounts frequently substantially exceed or underestimate actual ultimate costs
Solution Approach 1:
The patent implements preliminary action by pre-calculating adversely-developing predictors from historical claim data before actual claim assessment occurs. The machine learning module continuously processes historical data to establish predictive patterns in advance, so that when new claims need assessment, the system can quickly apply pre-established predictors rather than analyzing all historical data from scratch, thereby maintaining both accuracy and efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms where actual claim outcomes are fed back into the machine learning module to continuously refine and update the adversely-developing predictors. This feedback loop allows the system to learn from past prediction errors and improve future reserve accuracy while maintaining automated processing efficiency through iterative model optimization.
3Reliability
If carriers maintain higher loss reserve amounts to ensure solvency, then financial security is improved, but excessive reserves result in significant financial losses due to overestimation of actual costs
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting reserve amounts based on machine learning-generated predictors rather than using static or overly conservative estimates. The system calculates specific adversely-developing predictors for each claim based on historical patterns, allowing carriers to set reserves that are sufficiently high to ensure solvency but precisely calibrated to avoid systematic overestimation and associated financial losses.
4Reliability
If claims are settled early to reduce exposure to adverse development, then risk mitigation is improved, but premature settlement may result in paying more than necessary for claims that would have developed favorably
Solution Approach 1:
The patent implements dynamics by making settlement recommendations adaptive rather than static. The system continuously updates adversely-developing predictors based on the most recent historical data and claim characteristics, allowing settlement timing decisions to be dynamically optimized. Claims with high adverse development probability receive early settlement recommendations, while low-risk claims are monitored longer, balancing risk mitigation with cost efficiency.
Data Source
AI summary
Systems and methods for administering a workers' compensation (WC) claim include a non-transitory, tangible computer-readable storage medium including a WC claim processing program bearing instructions for performing a settlement strategy for WC claims. A processor is configured to execute the WC claim processing program. The WC claim processing program includes a Cost of Waiting (COW) calculating module configured to calculate a COW for a predetermined period of time after an Arrival of a Settlement Opportunity (ASO) of each of the population of seasoned WC claims using the seasoned WC claim financial data and a machine learning module configured to conduct a regression analysis of the population of seasoned WC claims to determine a WC claim characteristic comprising an adversely-developing predictor that the COW of an open WC claim will more likely develop adversely when the corresponding WC claim characteristic data of the open WC claim matches the adversely-developing predictor.