Predictive Possession Value Model for Insurance Claims
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Solution Overview
Problem
Users face difficulties in accurately valuing personal possessions for insurance purposes, leading to arduous inventory creation and potential inaccuracies, which affect insurance premiums and claims processing.
Innovation Solution
A computing system generates a predictive possession value model based on historical policyholder records, using personal and property data to estimate the value of a candidate user's possessions, determining a maximum reimbursement amount, and adjusting it during claims events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually create inventory lists of personal possessions, then the lists may include detailed item information, but the process is very time consuming and the lists may not be accurate
Solution Approach 1:
The system automatically generates inventory lists by analyzing data from social media posts, photos, and user profiles without requiring manual user input. The computer system performs self-service by autonomously identifying possessions, estimating their values, and maintaining updated inventory lists, thereby eliminating the time-consuming manual creation process while maintaining accuracy through multiple data source validation.
Solution Approach 2:
The manual mechanical process of users physically creating and updating inventory lists is replaced by an automated computer-based system that uses machine learning models, data analysis algorithms, and automated valuation methods. This substitution eliminates human effort and time while providing consistent, accurate results through systematic data processing.
2Reliability
If insurance providers require complete inventory lists for accurate coverage determination, then the coverage accuracy improves, but the complexity of the insurance process increases
Solution Approach 1:
The system extracts only the essential information needed for insurance coverage determination from extensive user data sources. Instead of requiring users to provide complete detailed inventories, the system extracts key possession categories, values, and replacement costs directly from social media data, photos, and automated assessments, simplifying the insurance process while maintaining coverage accuracy.
Solution Approach 2:
The system changes the parameters of inventory assessment from requiring detailed item-by-item user input to using automated data sources with different parameter sets (social media engagement metrics, photo analysis, profile information). This parameter transformation maintains the reliability of coverage determination while reducing process complexity by shifting from manual to automated data collection methods.
3Measurement precision
If users provide detailed inventory lists with item values, then the insurance premiums and coverage can be accurately determined, but the burden on users to maintain these lists increases
Solution Approach 1:
The system performs self-service by automatically maintaining inventory lists through continuous monitoring of user social media activity, photo uploads, and profile changes. The computer system autonomously updates possession inventories and value assessments without requiring user intervention, thereby maintaining accurate item values for insurance purposes while eliminating the operational burden on users.
Solution Approach 2:
The system implements continuous automated updates of inventory lists and value assessments by continuously analyzing new user data from social media sources. This continuous action ensures that insurance coverage accuracy is maintained over time without requiring periodic manual user updates, thereby preserving measurement precision while improving ease of operation through automation.
Data Source
AI summary
A computing system including a processor in communication with a memory device for generating a predicted one or more values of personal property items associated with a candidate user enrolling in an insurance policy may be provided. The processor may be configured to: (i) generate a predictive possession value model based at least in part upon a plurality of historical policyholder records, (ii) receive personal and property data associated with the candidate user, (iii) predict a one or more values associated with one or more items owned by the candidate user, (iv) determine a maximum reimbursement amount for the candidate user, (v) receive a claim associated with the candidate user in response to a claim event, wherein the claim includes a list of lost items and/or a list of spared items, (vi) estimate a value associated with the lists of lost items and/or spared items, (vii) adjust the maximum reimbursement amount based at least in part upon the estimated value, and (viii) determine an actual reimbursement amount for the candidate user.