Insurance Quote Comparison Using NLP for Risk Structure Visualization
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Solution Overview
Problem
The insurance broking process is hindered by the complexity and variability of insurance quote documents, leading to manual, time-consuming, and error-prone tasks in collating, analyzing, and recommending risk coverage options, which limits data-informed decision-making and misses capturing valuable market intelligence.
Innovation Solution
A system and method for ingesting, extracting, and organizing transactional information from unstructured and semi-structured insurance quote documents using natural language processing and machine learning to generate graphical comparisons and complex quote structures, providing objective and efficient risk coverage recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual quote collation and analysis is performed by brokers, then subjective expertise and market knowledge can be applied, but the process is time-consuming, error-prone, and biased
Solution Approach 1:
The patent replaces the manual mechanical process of quote collation and analysis with an automated computer-based system using optical character recognition (OCR), natural language processing (NLP), and machine learning algorithms. This substitution eliminates human error and bias while dramatically reducing processing time from days to minutes, directly resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The system enables self-service by automatically ingesting, processing, and analyzing quote documents without requiring broker intervention for data extraction and initial analysis. The automated platform independently performs document ingestion, data extraction, standardization, and comparison, freeing brokers from repetitive manual tasks while maintaining analysis quality.
2Adaptability or versatility
If multiple insurance carriers provide quotes in different formats and structures, then market competition and option diversity are enhanced, but the complexity of collation and standardization increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting data extraction and normalization parameters based on the specific format and structure of each incoming quote document. The system automatically adapts its processing parameters to handle different carriers' formats, maintaining flexibility while standardizing output. This resolves the contradiction by allowing format diversity at input while achieving uniformity at output through adaptive parameter adjustment.
Solution Approach 2:
The patent introduces an intermediary layer (the automated processing platform) between the diverse carrier quote formats and the broker's analysis needs. This intermediary performs format-agnostic ingestion, extracts relevant data using NLP and OCR, and transforms various structures into a unified standardized format, thereby managing complexity while preserving adaptability to different source formats.
3Loss of information
If brokers rely on subjective analysis and experience for placement decisions, then nuanced market understanding is applied, but objective data-informed decision-making is limited
Solution Approach 1:
The patent performs preliminary action by automatically capturing, storing, and analyzing all quote data including both accepted and rejected options before broker decision-making. The system pre-processes and structures market intelligence data, creating an objective factual foundation that complements broker expertise. This resolves the contradiction by preserving complete market information while enabling more informed decisions through automated data preparation and analysis.
Data Source
AI summary
In an illustrative embodiment, systems and methods for extracting, and organizing, and visualizing details of options provided by multiple organizations responsive to a risk fulfillment request include analyzing unstructured electronic documents to recognize various quote aspects in their contents, label the quote aspects according to a classification, and store semantically linked quote aspects. The systems and methods, for example, may enhance semantically-linked groups of quote aspects with attributes according to a corresponding ontology, and confirm the labeling, grouping, and enhancing through feedback interactions performed with a user via a graphical display. The confirmed information may be used to generate a visualization of options for fulfilling the request, each option qualified and/or color-coded through automated learned analysis for review by the user.