Automated Insurance Underwriting via Case-Based Reasoning
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Solution Overview
Problem
The insurance underwriting process is plagued by variability and inconsistency due to subjective underwriter judgment, ambiguous underwriting standards, and the need for manual processing of non-standard information, such as attending physician statements, which hinders automation and increases cycle time.
Innovation Solution
A system that compares pending insurance applications to previous decisions using fuzzy logic and case-based reasoning to adapt solutions, ranking similarities and applying confidence factors for reliable classification, thereby automating the underwriting process and reducing variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual underwriting is used, then subjectivity and variability are introduced, but flexibility in handling unique cases is maintained
Solution Approach 1:
The system copies and adapts solutions from previous underwriting decisions (case-based reasoning) to handle new applications. By retrieving and adapting past decisions, the system maintains flexibility for unique cases while ensuring consistency through standardized copying of proven solutions.
Solution Approach 2:
The system uses fuzzy logic to dynamically adjust underwriting parameters and standards based on the specific characteristics of each application. This allows the system to adapt to unique cases while maintaining reliable decision-making through structured parameter adjustments rather than subjective judgment.
2Adaptability or versatility
If underwriting standards are made detailed and comprehensive, then coverage of edge cases improves, but ambiguity and self-contradiction increase
Solution Approach 1:
The system segments the underwriting process into distinct modules: information retrieval, similarity calculation, case adaptation, and decision rendering. This segmentation allows detailed coverage of edge cases through specialized handling while reducing overall ambiguity by organizing complexity into manageable, non-overlapping components.
Solution Approach 2:
The system introduces an intermediary layer of case-based reasoning between the underwriting standards and final decisions. This intermediary retrieves and adapts previous decisions, acting as a mediator that translates detailed standards into specific applications without propagating ambiguity or self-contradiction in the standards themselves.
3Productivity
If automated underwriting is implemented, then consistency and speed improve, but handling of non-standard information like APS becomes problematic
Solution Approach 1:
The system implements a universal case-based reasoning framework that can handle both standard structured data and non-standard information like APS. By retrieving and adapting previous decisions, the system universally applies to various data types, maintaining productivity while adapting to non-standard information through analogy rather than requiring separate processing paths.
4Measurement precision
If human underwriters read and analyze APS manually, then accurate assessment of medical conditions is achieved, but cycle time increases significantly
Solution Approach 1:
The system copies and adapts solutions from previous APS analyses to assess medical conditions. By retrieving and adapting past assessments, the system achieves accurate evaluation without requiring manual re-reading of each APS, thus reducing cycle time while maintaining measurement precision through case-based reasoning.
Data Source
AI summary
A system for at least a partial underwriting of insurance policies is described. based on the similarity to previous insurance applications, a decision on the current request for underwriting may be made. this decision-making process represents an analogical approach to the placement of an insurance application to an underwriting category, whereby a given insurance application request is compared to previous requests.