Machine Learning Technologies for Efficiently Obtaining Insurance Coverage

Abstract

A computer-implemented method includes dividing consumers into multiple affinity groups based upon characteristics and preferences of the consumers, at least by analyzing the characteristics and/or preferences using a machine learning model. The method also includes auctioning an opportunity to provide insurance for one or more of the affinity groups, receiving one or more bids for purchase and/or offers of insurance for the one or more of the affinity groups, and accepting a winning bid. The method further includes causing individual insurance policies or a group insurance policy to be provide to or updated for consumers associated with a particular affinity group corresponding to the winning bid, thereby providing lower cost insurance and/or insurance that is more reflective of actual risk, or lack thereof, for the consumers associated with the particular affinity group

What is claimed: 
     
         1 . A computer-implemented method comprising:
 dividing, by one or more processors, a plurality of consumers into multiple affinity groups based at least upon one or more characteristics and one or more preferences of the plurality of consumers, at least in part by analyzing the one or more characteristics and/or the one or more preferences of the plurality of consumers using a machine learning model;   auctioning, by the one or more processors and via a communications network, an opportunity to provide insurance for one or more of the multiple affinity groups;   receiving, by the one or more processors and via the communications network, one or more bids for purchase and/or offers of insurance for the one or more of the multiple affinity groups;   accepting, by the one or more processors, a winning bid of the one or more bids; and   causing, by the one or more processors, individual insurance policies or a group insurance policy to be provided to or updated for consumers associated with a particular affinity group corresponding to the winning bid, thereby providing lower cost insurance and/or insurance that is more reflective of actual risk, or lack thereof, for the consumers associated with the particular affinity group.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein dividing the plurality of consumers into multiple affinity groups includes:
 determining risk scores for the plurality of consumers by analyzing the one or more characteristics of the plurality of consumers using the machine learning model; and   dividing the plurality of consumers into the multiple affinity groups based at least upon the risk scores and the one or more preferences of the plurality of consumers.   
     
     
         3 . The computer-implemented method of  claim 1 , wherein dividing the plurality of consumers into multiple affinity groups includes:
 determining preference classifications for the plurality of consumers by analyzing the one or more preferences of the plurality of consumers using the machine learning model; and   dividing the plurality of consumers into the multiple affinity groups based at least upon the preference classifications and the one or more characteristics of the plurality of consumers.   
     
     
         4 . The computer-implemented method of  claim 1 , wherein dividing the plurality of consumers into multiple affinity groups includes:
 determining classifications for the plurality of consumers by analyzing the one or more characteristics and the one or more preferences of the plurality of consumers using the machine learning model; and   dividing the plurality of consumers into the multiple affinity groups based at least upon the classifications.   
     
     
         5 . The computer-implemented method of  claim 1 , wherein dividing the plurality of consumers into multiple affinity groups includes:
 using the machine learning model to infer at least one additional characteristic and/or at least one additional preference for at least some of the plurality of consumers; and   dividing the plurality of consumers into the multiple affinity groups based at least in part upon the at least one additional characteristic and/or the at least one additional preference.   
     
     
         6 . The computer-implemented method of  claim 1 , further comprising, prior to dividing the plurality of consumers into the multiple affinity groups:
 determining, by the one or more processors analyzing historical data indicative of (i) consumer characteristics and/or preferences for different affinity groups and (ii) bidding activity for the different affinity groups, requirements for membership in each of the multiple affinity groups.   
     
     
         7 . The computer-implemented method of  claim 1 , wherein the machine learning model is a neural network, and further comprising, prior to dividing the plurality of consumers into the multiple affinity groups:
 training the neural network using historical data indicative of (i) consumer characteristics and/or preferences for different consumers and (ii) risk-related outcomes for the different consumers.   
     
     
         8 . The computer-implemented method of  claim 1 , wherein auctioning the opportunity to provide insurance for one or more of the multiple affinity groups includes:
 for each affinity group of the one or more of the multiple affinity groups, auctioning the opportunity to provide individual insurance policies for each consumer within the affinity group.   
     
     
         9 . The computer-implemented method of  claim 1 , wherein auctioning the opportunity to provide insurance for one or more of the multiple affinity groups includes:
 for each affinity group of the one or more of the multiple affinity groups, auctioning the opportunity to provide a group insurance policy for all consumers within the affinity group.   
     
     
         10 . The computer-implemented method of  claim 1 , further comprising, prior to dividing the plurality of consumers into the multiple affinity groups:
 receiving, by the one or more processors, vehicle telematics data for the plurality of consumers, the vehicle telematics data being indicative of one or more driving behaviors, and the one or more characteristics of the plurality of consumers including the one or more driving behaviors.   
     
     
         11 . A system comprising:
 a persistent memory storing a consumer profile database;   a communication interface configured to communicate with remote devices via a communications network;   one or more processors; and   one or more non-transitory, computer-readable media storing instructions that, when executed by the one or more processors, cause the system to
 divide a plurality of consumers into multiple affinity groups based at least upon one or more characteristics and one or more preferences of the plurality of consumers that are included in the consumer profile database, at least in part by analyzing the one or more characteristics and/or the one or more preferences of the plurality of consumers using a machine learning model, 
 auction, via the communication interface and the communications network, an opportunity to provide insurance for one or more of the multiple affinity groups, 
 receive, via the communication interface and the communications network, one or more bids for purchase and/or offers of insurance for the one or more of the multiple affinity groups, 
 accept a winning bid of the one or more bids, and 
 cause individual insurance policies or a group insurance policy to be provided to or updated for consumers associated with a particular affinity group corresponding to the winning bid, thereby providing lower cost insurance and/or insurance that is more reflective of actual risk, or lack thereof, for the consumers associated with the particular affinity group. 
   
     
     
         12 . The system of  claim 11 , wherein dividing the plurality of consumers into multiple affinity groups includes:
 determining risk scores for the plurality of consumers by analyzing the one or more characteristics of the plurality of consumers using the machine learning model; and   dividing the plurality of consumers into the multiple affinity groups based at least upon the risk scores and the one or more preferences of the plurality of consumers.   
     
     
         13 . The system of  claim 11 , wherein dividing the plurality of consumers into multiple affinity groups includes:
 determining preference classifications for the plurality of consumers by analyzing the one or more preferences of the plurality of consumers using the machine learning model; and   dividing the plurality of consumers into the multiple affinity groups based at least upon the preference classifications and the one or more characteristics of the plurality of consumers.   
     
     
         14 . The system of  claim 11 , wherein dividing the plurality of consumers into multiple affinity groups includes:
 determining classifications for the plurality of consumers by analyzing the one or more characteristics and the one or more preferences of the plurality of consumers using the machine learning model; and   dividing the plurality of consumers into the multiple affinity groups based at least upon the classifications.   
     
     
         15 . The system of  claim 11 , wherein dividing the plurality of consumers into multiple affinity groups includes:
 using the machine learning model to infer at least one additional characteristic and/or at least one additional preference for at least some of the plurality of consumers; and   dividing the plurality of consumers into the multiple affinity groups based at least in part upon the at least one additional characteristic and/or the at least one additional preference.   
     
     
         16 . The system of  claim 11 , wherein the instructions further cause the system to, prior to dividing the plurality of consumers into the multiple affinity groups:
 determine, by analyzing historical data indicative of (i) consumer characteristics and/or preferences for different affinity groups and (ii) bidding activity for the different affinity groups, requirements for membership in each of the multiple affinity groups.   
     
     
         17 . The system of  claim 11 , wherein the machine learning model is a neural network, and wherein the instructions further cause the system to, prior to dividing the plurality of consumers into the multiple affinity groups:
 train the neural network using historical data indicative of (i) consumer characteristics and/or preferences for different consumers and (ii) risk-related outcomes for the different consumers.   
     
     
         18 . The system of  claim 11 , wherein auctioning the opportunity to provide insurance for one or more of the multiple affinity groups includes:
 for each affinity group of the one or more of the multiple affinity groups, auctioning the opportunity to provide individual insurance policies for each consumer within the affinity group.   
     
     
         19 . The system of  claim 11 , wherein auctioning the opportunity to provide insurance for one or more of the multiple affinity groups includes:
 for each affinity group of the one or more of the multiple affinity groups, auctioning the opportunity to provide a group insurance policy for all consumers within the affinity group.   
     
     
         20 . The system of  claim 11 , wherein the instructions further cause the system to, prior to dividing the plurality of consumers into the multiple affinity groups:
 receive vehicle telematics data for the plurality of consumers, the vehicle telematics data being indicative of one or more driving behaviors, and the one or more characteristics of the plurality of consumers including the one or more driving behaviors.