Systems and methods for generating and updating a value of personal possessions of a user for insurance purposes

Abstract

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.

1 - 20 . (canceled) 
     
     
         21 . A computing system for generating one or more predicted values of one or more personal property items owned by a user, the computing system including one or more processors in communication with at least one memory device, the one or more processors configured to:
 generate a predictive possession value model based at least in part upon a plurality of historical policyholder records associated with a plurality of policyholders, by
 utilizing a machine learning model to predict one or more item values of one or more items owned by each policyholder of the plurality of policyholders based at least in part upon personal data and property data associated with each policyholder of the plurality of policyholders; 
   receive personal data and property data associated with the user;   determine, based at least in part upon the generated predictive possession model, the one or more predicted values of the one or more personal property items owned by the user based at least in part upon the received personal data and the received property data;   receive a claim associated with the user in response to a claim event, wherein the claim includes a list of lost items and a list of spared items;   estimate a claim value associated with the claim based at least in part on the list of lost items and the list of spared items; and   determine an actual reimbursement amount for the user based at least in part upon the one or more predicted values of the one or more personal property items and the estimated claim value associated with the claim.   
     
     
         22 . The computing system of  claim 21 , wherein to determine an actual reimbursement amount, the one or more processors are configured to:
 determine a maximum reimbursement amount for the user based at least in part upon the one or more predicted values; and   adjust the maximum reimbursement amount based at least in part upon the estimated claim value associated with the claim.   
     
     
         23 . The computing system of  claim 21 , wherein the one or more processors are further configured to:
 prompt the user to at least one of adjust the one or more predicted values and accept the one or more predicted values; and   store, in the at least one memory device, at least one of the one or more adjusted predicted values and the one or more accepted predicted values.   
     
     
         24 . The computing system of  claim 21 , wherein the plurality of historical policyholder records include (i) historical policy data including the one or more item values associated with the one or more items owned by each policyholder of the plurality of policyholders and historical insurance claim data associated with the plurality of policyholders, (ii) the personal data associated with each policyholder of the plurality of policyholders, and (iii) the property data associated with each policyholder of the plurality of policyholders 
     
     
         25 . The computing system of  claim 21 , wherein the one or more processors are further configured to:
 continually retrieve one or more additional historical policyholder records; and   update the predictive possession value model based at least in part upon the one or more additional historical policyholder records.   
     
     
         26 . The computing system of  claim 22 , wherein the one or more processors are configured to adjust the maximum reimbursement amount by:
 reducing the maximum reimbursement amount by one or more values associated with the list of spared items.   
     
     
         27 . The computing system of  claim 26 , wherein the one or more processors are further configured to adjust the maximum reimbursement amount by:
 estimating the one or more values associated with the list of spared items by at least comparing one or more values associated with the list of lost items to the one or more predicted values; and   reducing the maximum reimbursement amount by the one or more estimated values associated with the list of spared items.   
     
     
         28 . The computing system of  claim 21 , wherein the one or more processors are further configured to:
 provide the actual reimbursement amount to the user, wherein the actual reimbursement amount is provided in a form of at least one selected from a group consisting of a check, a direct deposit, cash, a digital wallet credit, and a prepaid card.   
     
     
         29 . A method for generating one or more predicted values of one or more personal property items owned by a user, the method implemented on a computer device including one or more processors in communication with at least one memory device, the method comprising:
 generating a predictive possession value model based at least in part upon a plurality of historical policyholder records associated with a plurality of policyholders, by
 utilizing a machine learning model to predict one or more item values of one or more items owned by each policyholder of the plurality of policyholders based at least in part upon personal data and property data associated with each policyholder of the plurality of policyholders; 
   receiving personal data and property data associated with the user;   determining, based at least in part upon the generated predictive possession model, the one or more predicted values of the one or more personal property items owned by the user based at least in part upon the received personal data and the received property data;   receiving a claim associated with the user in response to a claim event, wherein the claim includes a list of lost items and a list of spared items;   estimating a claim value associated with the claim based at least in part on the list of lost items and the list of spared items; and   determining an actual reimbursement amount for the user based at least in part upon the one or more predicted values of the one or more personal property items and the estimated claim value associated with the claim.   
     
     
         30 . The method of  claim 29  further comprising:
 determining a maximum reimbursement amount for the user based at least in part upon the one or more predicted values; and 
 adjusting the maximum reimbursement amount based at least in part upon the estimated claim value associated with the claim. 
 
     
     
         31 . The method of  claim 29  further comprising:
 prompting the user to at least one of adjust the predicted one or more values and accept the one or more predicted values; and 
 storing, in the at least one memory device, at least one selected from a group consisting of the one or more adjusted predicted values and the one or more accepted predicted values. 
 
     
     
         32 . The method of  claim 29  further comprising:
 providing the actual reimbursement amount to the user, wherein the actual reimbursement amount is provided in a form of at least one selected from a group consisting of a check, a direct deposit, cash, a digital wallet credit, and a prepaid card. 
 
     
     
         33 . The method of  claim 29  further comprising:
 continually retrieving one or more additional historical policyholder records; 
 updating the predictive possession value model based at least in part upon the one or more additional historical policyholder records; and 
 storing, in the at least one memory device, the updated predictive possession value model. 
 
     
     
         34 . The method of  claim 30 , wherein the adjusting the maximum reimbursement amount includes:
 reducing the maximum reimbursement amount by one or more values associated with the list of spared items.   
     
     
         35 . The method of  claim 34 , wherein the adjusting the maximum reimbursement amount includes:
 estimating the one or more values associated with the list of spared items by at least comparing one or more values associated with the list of lost items to the one or more predicted values; and   reducing the maximum reimbursement amount by the one or more estimated values of the list of spared items.   
     
     
         36 . One or more non-transitory computer-readable media having computer-executable instructions thereon, wherein the computer-executable instructions, when executed by one or more processors in communication with at least one memory device, cause the one or more processors to:
 generate a predictive possession value model based at least in part upon a plurality of historical policyholder records associated with a plurality of policyholders, by
 utilizing a machine learning model to predict one or more item values of one or more items owned by each policyholder of the plurality of policyholders based at least in part upon personal data and property data associated with each policyholder of the plurality of policyholders; 
   receive personal data and property data associated with a user;   determine, based at least in part upon the generated predictive possession model, one or more predicted values of one or more personal property items owned by the user based at least in part upon the received personal data and the received property data;   receive a claim associated with the user in response to a claim event, wherein the claim includes one of a list of lost items and a list of spared items;   estimate a claim value associated with the claim based at least in part on the list of lost items and the list of spared items; and   determine an actual reimbursement amount for the user based at least in part upon the one or more predicted values of the one or more personal property items and the estimated claim value associated with the claim.   
     
     
         37 . The one or more non-transitory computer-readable media of  claim 36 , wherein the computer-executable instructions further cause the one or more processors to:
 determine a maximum reimbursement amount for the user based at least in part upon the one or more predicted values; and   adjust the maximum reimbursement amount based at least in part upon the estimated claim value associated with the claim.   
     
     
         38 . The one or more non-transitory computer-readable media of  claim 36 , wherein the computer-executable instructions further cause the one or more processors to:
 prompt the user to at least one of adjust the one or more predicted values and accept the one or more predicted values; and   store, in the at least one memory device, at least one selected from a group consisting of the one or more adjusted predicted values and the one or more accepted predicted values.   
     
     
         39 . The one or more non-transitory computer-readable media of  claim 36 , wherein the computer-executable instructions further cause the one or more processors to:
 provide the actual reimbursement amount to the user, wherein the actual reimbursement amount is provided in a form of at least one selected from a group consisting of a check, a direct deposit, cash, a digital wallet credit, and a prepaid card.   
     
     
         40 . The one or more non-transitory computer-readable media of  claim 36 , wherein the computer-executable instructions further cause the one or more processors to:
 continually retrieve one or more additional historical policyholder records; and   update the predictive possession value model based at least in part upon the one or more additional historical policyholder records.