Automated Underwriting Confidence Factor via Fuzzy Logic
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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 like attending physician statements, which hinders automation and increases cycle time.
Innovation Solution
A process that determines a confidence factor for insurance applications by comparing them to previous underwritten cases, using fuzzy logic to adjust underwriting standards and translate conditional probabilities into soft constraints, enabling automated decision-making with a run-time function to evaluate confidence thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual reading and processing of APS documents is performed by human underwriters, then non-standard information can be evaluated, but cycle time is greatly extended and underwriter variability increases
Solution Approach 1:
The patent replaces the mechanical manual reading process with an automated natural language processing system that can extract and evaluate information from APS documents. The system uses text analysis algorithms to identify key medical information, impairments, and risk factors without requiring human underwriters to manually read each document, thereby dramatically reducing cycle time while maintaining the ability to process non-standard information.
Solution Approach 2:
The patent introduces an intermediary automated processing layer between the APS documents and the underwriting decision system. This intermediary system pre-processes the non-standard information, extracts relevant features, and structures them in a format suitable for automated evaluation, enabling the downstream system to handle non-standard information efficiently without requiring manual intervention.
2Productivity
If automated underwriting is implemented, then productivity increases, but the system cannot handle non-standard or non-sufficient information
Solution Approach 1:
The patent transforms unstructured non-standard information into structured parameters that can be processed by automated systems. By applying natural language processing and text extraction techniques, the system converts free-text APS documents into standardized data fields and risk parameters, enabling automated underwriting to handle previously unprocessable non-standard information while maintaining high productivity.
Solution Approach 2:
The patent creates a universal information processing framework that can handle both structured and unstructured data types. The system is designed to process multiple formats of input information including standard forms, non-standard APS documents, and various data sources, unifying them into a common processing pipeline that maintains productivity while expanding adaptability to handle diverse information types.
3Adaptability or versatility
If subjective judgment of underwriters is used, then flexibility in interpreting underwriting standards is achieved, but variability and inconsistency in decisions increase
Solution Approach 1:
The patent implements a feedback mechanism where the automated system learns from historical underwriting decisions and continuously refines its evaluation criteria. By analyzing patterns in experienced underwriters' decisions while maintaining consistency through systematic application of learned rules, the system captures interpretation flexibility while eliminating variability, achieving reliable and consistent decisions that reflect expert judgment.
Solution Approach 2:
The patent performs preliminary analysis and evaluation of information sources before the actual underwriting decision is made. The system pre-identifies relevant risk factors, pre-evaluates information quality, and pre-structures data in a way that enables consistent application of underwriting standards, thereby ensuring decision consistency while preserving the ability to flexibly interpret standards based on the pre-processed information.
Data Source
AI summary
A process is described for evaluating the decision-making confidence of a process and system for at least a partial underwriting of insurance policies where placement of an insurance application to an underwriting category is based on its similarity to previous insurance applications. The confidence factor computed is a measure of the correctness of the decision for a given application for insurance.