Systems and methods for computing and applying consumer value scores to electronic transactions

Abstract

Systems and methods are disclosed for generating a consumer value score. One method includes: receiving a user identifier associated with a first user; receiving a designation of a period of time for analysis; receiving transaction data associated with the user identifier, the transaction data including a record of one or more transactions, wherein each transaction is associated with the user identifier, a merchant, a transaction time, and a purchase amount; identifying, of the received transaction data, a set of transactions conducted during the received period of time, based on the transaction time of each transaction; identifying, of the set of transactions, purchase data related to a given merchant; determining, of the purchase data related to a given merchant, a value of a purchase amount; and generating a consumer value score based on the determined value of the purchase amount.

1 - 20 . (canceled) 
     
     
         21 . A computer-implemented method for authorizing a potential fraudulent transaction, the method comprising:
 receiving an authorization request for a transaction from an interactive interface that prompts a user to select a plurality of categories of products;   identifying a plurality of data associated with the user based, at least in part, on a user identifier associated with the user, wherein the plurality of data are encrypted;   processing the plurality of data to determine instances of fraudulent behaviors to predict a potential fraudulent behavior;   calculating a score for the user based, at least in part, on the processing of the plurality of data;   comparing the calculated score to a pre-determined score threshold to determine the calculated score exceeds the pre-determined score threshold; and   authorizing the potential fraudulent transaction based, at least in part, on the calculated score.   
     
     
         22 . The computer-implemented method of  claim 21 , wherein the calculated score includes a product spend score indicating an average spending pattern of the user on a particular product during a given period of time, a category spend score indicating one or more average spending patterns by the user on the plurality of categories of products during the given period of time, or a combination thereof. 
     
     
         23 . The computer-implemented method of  claim 21 , wherein the instances of fraudulent behaviors include discrepancy in location information with stored location information of the user, an unusually large transaction information that is inconsistent with historical spending patterns of the user, a purchasing pattern that conflicts with historical purchasing patterns of the user, or a combination thereof. 
     
     
         24 . The computer-implemented method of  claim 21 , further comprising:
 determining a discrepancy between the authorization request and the calculated score of the user;   comparing the calculated score to the pre-determined score threshold to determine the calculated score is below the pre-determined score threshold; and   prompting a denial of the potential fraudulent transaction based, at least in part, on the comparison.   
     
     
         25 . The computer-implemented method of  claim 22 , further comprising:
 determining an operation of at least one service provider based, at least in part, on the product spend score, the category spend score, or a combination thereof,   wherein the operation includes monitoring, replacing, stocking, or a combination thereof of one or more products in an inventory of the at least one service provider.   
     
     
         26 . The computer-implemented method of  claim 21 , further comprising:
 generating a profile information for the user based, at least in part, on the plurality of data associated with the user, wherein the profile information includes a unique identifier hash, primary account number (PAN), personally identifiable information (PII), an analysis of spending habit of the user, location information of the user, a fraudulent activities reports on the PAN, or a combination thereof; and   storing the profile information of the user in a profile database, wherein the profile information is tokenized.   
     
     
         27 . The computer-implemented method of  claim 26 , further comprising:
 searching the profile database for the plurality of data associated with the user based, at least in part, on the authorization request; and   retrieving the plurality of data associated with the user to predict the potential fraudulent behavior and calculate the score.   
     
     
         28 . The computer-implemented method of  claim 21 , further comprising:
 determining a fraud risk for at least one service provider, wherein the fraud risk includes a potential, an actual, or a combination thereof fraudulent behaviors associated with the user; and   calculating the score based, at least in part, on the fraud risk, wherein the score decreases upon determining the potential, the actual, or a combination thereof fraudulent behaviors associated with the user.   
     
     
         29 . The computer-implemented method of  claim 21 , further comprising:
 detecting a frequency of one or more transactions associated with the user; and   calculating the score based, at least in part, on the frequency of the one or more transactions, wherein the score increases upon determining a higher frequency of the one or more transactions.   
     
     
         30 . The computer-implemented method of  claim 21 , further comprising:
 calculating the score based, at least in part, on averaging spending habit of the user over a plurality of time periods;   comparing the calculated score to a population-based average score to determine the calculated score exceeds the population-based average score; and   prompting an approval of the potential fraudulent transaction based on the comparison.   
     
     
         31 . A decentralized computer system for authorizing a potential fraudulent transaction, the method comprising:
 receiving an authorization request for a transaction from an interactive interface that prompts a user to select a plurality of categories of products;   identifying a plurality of data associated with the user based, at least in part, on a user identifier associated with the user, wherein the plurality of data are encrypted;   processing the plurality of data to determine instances of fraudulent behaviors to predict a potential fraudulent behavior;   calculating a score for the user based, at least in part, on the processing of the plurality of data;   comparing the calculated score to a pre-determined score threshold to determine the calculated score exceeds the pre-determined score threshold; and   authorizing the potential fraudulent transaction based, at least in part, on the calculated score.   
     
     
         32 . The decentralized computer system of  claim 31 , wherein the calculated score includes a product spend score indicating an average spending pattern of the user on a particular product during a given period of time, a category spend score indicating one or more average spending patterns by the user on the plurality of categories of products during the given period of time, or a combination thereof. 
     
     
         33 . The decentralized computer system of  claim 31 , wherein the instances of fraudulent behaviors include discrepancy in location information with stored location information of the user, an unusually large transaction information that is inconsistent with historical spending patterns of the user, a purchasing pattern that conflicts with historical purchasing patterns of the user, or a combination thereof. 
     
     
         34 . The decentralized computer system of  claim 31 , further comprising:
 determining a discrepancy between the authorization request and the calculated score of the user;   comparing the calculated score to the pre-determined score threshold to determine the calculated score is below the pre-determined score threshold; and   prompting a denial of the potential fraudulent transaction based, at least in part, on the comparison.   
     
     
         35 . The decentralized computer system of  claim 32 , further comprising:
 determining an operation of at least one service provider based, at least in part, on the product spend score, the category spend score, or a combination thereof,   wherein the operation includes monitoring, replacing, stocking, or a combination thereof of one or more products in an inventory of the at least one service provider.   
     
     
         36 . The decentralized computer system of  claim 31 , further comprising:
 generating a profile information for the user based, at least in part, on the plurality of data associated with the user, wherein the profile information includes a unique identifier hash, primary account number (PAN), personally identifiable information (PII), an analysis of spending habit of the user, location information of the user, a fraudulent activities reports on the PAN, or a combination thereof; and   storing the profile information of the user in a profile database, wherein the profile information is tokenized.   
     
     
         37 . The decentralized computer system of  claim 36 , further comprising:
 searching the profile database for the plurality of data associated with the user based, at least in part, on the authorization request; and   retrieving the plurality of data associated with the user to predict the potential fraudulent behavior and calculate the score.   
     
     
         38 . A non-transitory machine-readable medium storing instructions that, when executed by a server, cause the server to perform a method for authorizing a potential fraudulent transaction, the method comprising:
 receiving an authorization request for a transaction from an interactive interface that prompts a user to select a plurality of categories of products;   identifying a plurality of data associated with the user based, at least in part, on a user identifier associated with the user, wherein the plurality of data are encrypted;   processing the plurality of data to determine instances of fraudulent behaviors to predict a potential fraudulent behavior;   calculating a score for the user based, at least in part, on the processing of the plurality of data;   comparing the calculated score to a pre-determined score threshold to determine the calculated score exceeds the pre-determined score threshold; and   authorizing the potential fraudulent transaction based, at least in part, on the calculated score.   
     
     
         39 . The non-transitory machine-readable medium of  claim 38 , wherein the calculated score includes a product spend score indicating an average spending pattern of the user on a particular product during a given period of time, a category spend score indicating one or more average spending patterns by the user on the plurality of categories of products during the given period of time, or a combination thereof. 
     
     
         40 . The non-transitory machine-readable medium of  claim 38 , wherein the instances of fraudulent behaviors include discrepancy in location information with stored location information of the user, an unusually large transaction information that is inconsistent with historical spending patterns of the user, a purchasing pattern that conflicts with historical purchasing patterns of the user, or a combination thereof.