Underwriting Rule Extraction Using Neural Risk Parameters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing insurance underwriting processes are inefficient and resource-intensive, relying heavily on manual review and analysis of underwriting manuals, which lack transparency and accuracy.
Innovation Solution
An insurance analytics system utilizing neural network models, particularly transformer-based language models, to extract rules and determine risk parameters from underwriting manuals, providing user interfaces for review and modification, and generating risk summaries for underwriters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual review and analysis of underwriting manuals is used, then human judgment and flexibility are maintained, but the process becomes inefficient and resource-intensive
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated system using neural network models and natural language processing to extract rules and determine risk parameters from underwriting manuals, thereby improving efficiency while reducing human labor requirements
Solution Approach 2:
The patent introduces an intermediary automated underwriting system that acts as a bridge between underwriting manuals and decision-making, using neural networks to process and interpret manual content, thus reducing direct human involvement in repetitive analysis tasks
2Measurement precision
If manual review processes are used, then human expertise is applied, but transparency and accuracy are reduced
Solution Approach 1:
The patent implements feedback mechanisms where the automated system's extractions of rules and risk parameters are made visible and interpretable to users, allowing verification and adjustment, thus maintaining transparency while improving measurement precision through systematic processing
Solution Approach 2:
The patent creates structured digital copies of unstructured underwriting manual content through neural network extraction, transforming ambiguous text into precise, machine-readable rules and parameters that improve accuracy while maintaining traceability to original sources
3Productivity
If automated neural network models are used, then capacity and accuracy are enhanced, but device complexity increases
Solution Approach 1:
The patent employs universal neural network models that can process multiple types of underwriting manuals and extract various kinds of rules and parameters through a single system architecture, thereby increasing processing capacity without proportionally increasing overall system complexity
Solution Approach 2:
The patent enables the neural network system to automatically adapt and learn from underwriting manual content without requiring complex manual configuration, allowing the system to self-configure extraction rules and parameters, thus reducing operational complexity despite increased processing capacity
Data Source
AI summary
Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for extracting rules and determining risk parameters from an underwriting manual. The program and method provide for receiving an underwriting manual for an insurance carrier, wherein the underwriting manual comprises text describing rules, guidelines and parameters defining a risk appetite of the insurance carrier; processing the underwriting manual using one or more neural network models, the one or more neural network models having been trained with domain-specific data to analyze text across plural underwriting manuals; determining, based on the processing, insurance rules and risk parameters for the text in the underwriting manual; and generating a set of rules based on the insurance rules and risk parameters.