Predictive Model Segmentation Platform for Disability Claims
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manually assigning disability insurance claims to claim handlers is time-consuming and inefficient, especially when dealing with a large number of claims, as it does not effectively distribute workload and complexity among handlers.
Innovation Solution
A triage platform that receives data on disability insurance claims, determines a claim segment based on characteristics, and automatically assigns a claim handler, routing information for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If claims are assigned using random or round robin method, then assignment process is simple, but workload distribution becomes unbalanced and processing efficiency decreases
Solution Approach 1:
The system changes the assignment parameters from simple random/round-robin methods to a multi-factor evaluation system that considers claim complexity, handler expertise, current workload, and specialization. This transforms the assignment process from a single-parameter (simple rotation) to a multi-parameter optimization system, resolving the contradiction between simplicity and efficiency.
Solution Approach 2:
The system performs preliminary classification of claims into segments based on complexity and characteristics before assignment. By pre-categorizing claims and pre-evaluating handler capabilities, the system prepares the data structure needed for optimized assignment, eliminating the need for complex real-time decisions during the assignment moment itself.
2Measurement precision
If manual determination of claim handler assignment is used, then assignment accuracy improves, but time consumption increases significantly
Solution Approach 1:
The system replaces the mechanical manual review process with an automated computerized assignment system that uses algorithms to evaluate claim characteristics, segment claims, and match handlers. This substitution maintains high assignment accuracy through systematic evaluation while eliminating the time-consuming manual review of each claim.
Solution Approach 2:
The system creates a digital model or representation of the manual assignment process, capturing the decision-making logic and criteria in software. This digital copy automates the evaluation and matching process, preserving the accuracy of expert judgment while removing the time constraint of human review.
3Measurement precision
If complex evaluation system is implemented for claim assignment, then assignment accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex assignment problem into manageable components: claim segmentation (categorizing claims by complexity and type), handler segmentation (grouping handlers by expertise and capacity), and evaluation segmentation (separate scoring for different factors). This modular segmentation reduces overall system complexity while maintaining comprehensive evaluation accuracy.
Data Source
AI summary
A segmentation platform is interposed between an intake server and remote user-accessible computers. The segmentation platform includes a model training computer processor configured to generate a predictive model for determining a segment corresponding to received data sets based upon claim segment rules and historical data sets stored in data storage devices. The segmentation platform may be configured to dynamically update workload data corresponding to a plurality of users, and responsive to that updating and based upon application of the predictive model, segment the received current data sets and transmit electronic claim files to respective remote user-accessible computing devices. The predictive model is continually adapted through activation of the model training component to update the predictive model with new current claim data sets.