Parameter-Pattern Data Mining for Risk-Transfer Policy Gap Detection
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Solution Overview
Problem
Existing systems struggle to efficiently detect and address gaps in life risk-transfer coverage for individuals due to the complexity of risk-transfer terminologies, policies, and the need for expert consultation to assess coverage, leading to inadequate protection for many individuals.
Innovation Solution
An automated system using a parameter pattern-driven, data mining approach with a customizable chain of machine-learning structures to process and parse digital risk-transfer policies, translating contractual language and measuring data to generate personalized risk-transfer policies, incorporating wearable and telematics data for gap detection and automated underwriting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems are used to process risk-transfer policies, then processing efficiency is improved, but the complexity of machine-learning structures increases
Solution Approach 1:
The machine-learning system is divided into multiple specialized models, each handling a specific task such as policy parsing, risk assessment, and gap detection. This segmentation allows each model to be optimized for its specific function while reducing the overall complexity management burden.
Solution Approach 2:
The system employs a universal data processing framework that can handle multiple types of risk-transfer policies and data formats through a common architecture, reducing complexity by reusing the same processing infrastructure across different applications.
2Measurement precision
If expert consultation is used to assess coverage, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The machine-learning system performs self-assessment of risk-transfer coverage by automatically analyzing policy documents and customer data, eliminating the need for manual expert review while maintaining high accuracy through sophisticated algorithms.
Solution Approach 2:
The system incorporates feedback mechanisms where the results of coverage assessment are continuously refined through comparison with historical data and expert-validated patterns, improving measurement precision over time while maintaining automated processing speeds.
3Measurement precision
If comprehensive risk-transfer policies are processed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
Comprehensive policies are broken down into discrete analytical components, with separate machine-learning models dedicated to parsing specific policy clauses, assessing risk parameters, and identifying coverage gaps. This modular approach maintains high measurement precision while managing system complexity through specialized functionality.
Data Source
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AI summary
Proposed is a parameter pattern-driven, data mining system and corresponding method with a knowledge extraction engine based on a customizable chain of machine-learning-structures providing an automated pipeline for data processing of complex data structures with a hidden pattern detection for triggering automated under-writing processes. A plurality of digital risk-transfer policies is assessed via a data interface and storable captured by a persistence repository unit of the parameter pattern-driven, data mining system. The digital policy at least comprises premium parameter values and/or deducible parameter values and/or risk-transfer type definition parameter values and/or policy limits parameter values and/or exclusion parameter values and/or riders/addit parameter values. The parameter pattern-driven, data mining system comprises a chained series of machine learning modeling structures automatically assessing and parsing digital risk-transfer policies of a policyholder, and automatically translating contractual language of the digital policy into actionable offers for the policyholder by generating appropriate new digital risk-transfer policies for automated under writing by the policyholder.