Automated Underwriting Triage for Real-Time Smoking Risk Classification
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Solution Overview
Problem
Existing automated underwriting systems for life insurance struggle with complex, time-consuming, and inconsistent risk assessment processes, particularly for preferred life insurance systems, which require manual intervention and lack real-time, mortality-consistent risk transfer capabilities, leading to inefficiencies and inconsistencies in risk classification and management.
Innovation Solution
An automated, real-time mortality classification and signaling system using a selective multi-level triage process that includes a machine learning-based pattern recognition module to categorize individuals as smokers or non-smokers, with optional laboratory tests only for a small percentage, allowing for rapid, 'fluid-less' underwriting and optimized risk transfer parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human experts perform traditional underwriting, then subjective judgment and experience can be applied, but consistency and reliability of decisions deteriorate
Solution Approach 1:
The underwriting system performs self-assessment by automatically evaluating risk factors and making underwriting decisions without human intervention. The system uses predefined underwriting rules and algorithms to assess applications, eliminating subjective judgment variability while maintaining decision quality through automated consistency.
Solution Approach 2:
The patent replaces the mechanical human decision-making process with an automated computational system. Human underwriters are substituted by computer-based underwriting engines that process applications using consistent algorithms, thereby eliminating the inconsistency inherent in human judgment while preserving adaptability through programmable underwriting rules.
2Measurement precision
If the number of features and rules/standards increases, then risk assessment accuracy improves, but time consumption and difficulty increase
Solution Approach 1:
The underwriting process is segmented into distinct automated stages: data collection, risk factor identification, rule-based evaluation, and decision rendering. Each segment handles specific aspects of the underwriting process, allowing complex multi-feature assessment to be processed systematically and efficiently without manual intervention at each step.
Solution Approach 2:
The patent introduces an automated underwriting engine as an intermediary between the application data and the underwriting decision. This intermediary systematically processes numerous features and rules through predefined algorithms, eliminating the time-consuming manual analysis while maintaining comprehensive risk assessment accuracy.
3Productivity
If automated underwriting is implemented, then productivity and speed improve, but handling of complex non-linear processes deteriorates
Solution Approach 1:
The underwriting system employs dynamic rule engines that can adaptively apply different underwriting rules and algorithms based on the specific characteristics of each application. The system dynamically adjusts the complexity of the assessment process, applying simple rules for straightforward cases and more complex multi-factor analysis for unusual or high-risk applications, thereby maintaining both speed and handling capability.
Solution Approach 2:
The patent implements a nested structure where multiple layers of underwriting rules and evaluation criteria are embedded within each other. Simple underwriting rules form the base layer, with more complex rules nested within for specific risk scenarios. This nested architecture allows the system to process simple cases quickly while automatically invoking deeper, more complex evaluation layers only when necessary, preserving both productivity and complex process handling.
Data Source
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AI summary
Proposed are an automated, distinct triage or channel-based (81,82,83) automated mortality classification and measuring system (1) and a method for the automated assessment, measurement, and monitoring of life risks (9), wherein life risks associated with risk-exposed individuals (91, 92, 93) are transferable to a first insurance system (2) and/or from the first insurance system (2) to an associated second insurance system (3). The system (1) comprises a table (10) with retrievable stored risk classes (101, 102, 103) each comprising assigned, triggerable risk class criteria (110, 111, 112). Individual-specific parameters (911, 921, 931) of the risk-exposed individuals (91, 92, 93) are captured relating to criteria (110, 111, 112) of the stored risk classes (101, 102, 103) and a specific risk class (101, 102, 103) associated with the risk of the exposed individual (31, 32, 33) is identified and selected from said stored risk classes (101, 102, 103) based on the captured parameters (311, 321, 331). The individual-specific parameters (911, 921, 931) comprise at least parameters indicating a captured self-declaration of smoking or non-smoking. Upon triggering (71 /711) parameters (916, 926, 936) indicating a captured self-declaration of smoking (8111, 8112, 8113), the risk-exposed individual (91, 92, 93) is automatically assigned to a first triage channel (81). However, upon triggering (71 /712) of a captured self-declaration of non-smoking, the triggered individual-specific parameters (911, 921, 931) are processed by a machine learning- based pattern recognition module (8) automatically assigning risk-exposed individuals (91, 92, 93) with detected non-smoking patterns (8211, 8212, 8213) to a second triage channel (82) as predicted non-smokers, and automatically assigning risk-exposed individuals (91, 92, 93) with detected smoking patterns (8311, 8312, 8313) to a third triage channel (83) as predicted smokers. For risk-exposed individuals (91, 92, 93) in the third triage channel (83), laboratory-scaled individual-specific parameters (915, 925, 935) are measured by means of laboratory measuring devices (914, 924, 934), and the laboratory-scaled individual-specific parameters (915, 925, 935) are triggered for measured smoking and not-measured smoking.