Computer-Based Risk Control Analysis With Coverage Matrices
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Solution Overview
Problem
Insurance companies face challenges in streamlining claims processing and improving risk assessment due to the increasing complexity of insurance products and regulatory frameworks, which can impact profitability, customer satisfaction, and compliance with regulatory requirements.
Innovation Solution
A computer-implemented method and system that includes receiving client attribute data, identifying relevant coverage lines, generating a solution matrix, and providing a graphical user interface (GUI) with detailed information, vendor comparisons, and ROI analysis to assist in making informed decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual claims processing and risk assessment methods are used, then detailed human analysis can be performed, but operational efficiency is reduced and processing time increases
Solution Approach 1:
The patent replaces manual mechanical analysis processes with an automated computer-based system that uses algorithms and data processing to perform claims handling and risk assessment. The system automatically ingests client attribute data, identifies coverage lines, generates solution matrices, and provides vendor comparisons without requiring manual intervention for each analysis step, thereby dramatically improving processing efficiency and reducing time loss.
2Measurement precision
If comprehensive risk assessment and vendor analysis are performed, then decision accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex risk assessment process into distinct modular components: data ingestion module, coverage line identification module, solution matrix generation module, vendor scoring module, and ROI analysis module. Each module handles a specific aspect of the assessment, processing data independently and passing results to the next stage. This segmentation maintains high assessment accuracy while reducing overall system complexity through clear separation of concerns and independent module development.
Solution Approach 2:
The patent introduces a structured solution matrix as an intermediary data structure that organizes and standardizes risk assessment results before presenting them to users. The matrix serves as a mediator between the complex backend analysis processes and the simplified user interface, translating complex vendor scores, viability factors, and ROI calculations into an easily interpretable format that maintains precision while reducing perceived complexity.
3Loss of information
If detailed vendor comparison and ROI analysis are provided, then informed decision-making is enabled, but information processing requirements increase
Solution Approach 1:
The patent performs preliminary vendor scoring and viability factor analysis before the user needs to make decisions. The system pre-calculates vendor scores based on multiple criteria (product relevance, performance history, pricing, discounts) and pre-generates ROI projections for different coverage scenarios. This preliminary processing consolidates complex computational tasks into background operations, reducing real-time information processing requirements while ensuring complete decision information is available when needed.
Solution Approach 2:
The patent transforms complex vendor and solution data into standardized parametric formats with defined weightings and scoring ranges. By converting qualitative vendor attributes into quantifiable parameters with consistent measurement scales, the system enables efficient comparison and analysis. This parameterization reduces information processing requirements by establishing uniform data structures that can be rapidly computed and compared, while still providing comprehensive decision information through the structured presentation of multiple weighted factors.
Data Source
AI summary
A method, computer program product, and computing system for receiving client attribute data, identifying one or more relevant coverage lines based on the received client attribute data, and generating a solution matrix for each of the one or more relevant coverage lines.