Performing an action based on predicted information

Abstract

A device may obtain user information associated with a user and first account information associated with the user. The device may determine, based on the user information, user employment information and may determine, based on the first account information, user compensation information. The device may process, using a first machine learning model, the user employment information and the user compensation information to determine predicted future user compensation information. The device may obtain second account information associated with the user and may determine, based on the second account information, new user compensation information. The device may determine whether the new user compensation information is consistent with the predicted future user compensation information. The device may determine a predicted reason for the new user compensation information not being consistent with the predicted future user compensation information. The device may cause, based on the predicted reason, at least one action to be performed.

What is claimed is: 
     
         1 . A method, comprising:
 receiving, by a first device, from a second device, a first machine learning model,
 wherein the first machine learning model is trained based on at least a neural network technique that performs pattern recognition associated with historical information related to a monetary amount, and 
 wherein the monetary amount is associated with a first account; 
   processing, by the first device and using the first machine learning model, monetary amount and employment information to determine a predicted monetary amount;   determining, by the first device, whether a new monetary amount associated with a second account is consistent with the predicted monetary amount;   determining, by the first device and using a second machine learning model, a predicted reason for the new monetary amount not being consistent with the predicted monetary amount;   identifying, by the first device, a third device associated with an organization related to at least one of the first account or the second account; and   establishing, by the first device, a communication session between the third device and a fourth device.   
     
     
         2 . The method of  claim 1 , wherein the first machine learning model is received based on a parameter that is associated with at least one of:
 scheduling,   an on-demand basis, or   a trigger.   
     
     
         3 . The method of  claim 2 , wherein the first machine learning model is trained and updated by the second device. 
     
     
         4 . The method of  claim 1 , wherein the predicted monetary amount comprises at least one of a predicted compensation amount or a predicted compensation date. 
     
     
         5 . The method of  claim 1 , wherein additional information is obtained from crawling one or more information sources that are not associated with the first account or the second account, and
 wherein the predicted reason is determined based on the additional information.   
     
     
         6 . The method of  claim 1 , wherein the second machine learning model is trained based on historical additional information associated with a plurality of user accounts. 
     
     
         7 . The method of  claim 1 , wherein the communication session is established based on causing the third device to send instructions to the fourth device for initiating the communication session. 
     
     
         8 . A first device, comprising:
 one or more memories; and   one or more processors, coupled to the one or more memories, configured to:
 receive, from a second device, a first machine learning model,
 wherein the first machine learning model is trained based on at least a neural network technique that performs pattern recognition associated with historical information related to a monetary amount, and 
 wherein the monetary amount is associated with a first account; 
 
 process, based on the first machine learning model, a monetary amount and employment information to determine a predicted monetary amount; 
 determine whether a new monetary amount associated with a second account is consistent with the predicted monetary amount; 
 determine, based on a second machine learning model, a predicted reason for the new monetary amount not being consistent with the predicted monetary amount; and 
 establish a communication session between a third device, associated with the at least one of the first account or the second account, and a fourth device. 
   
     
     
         9 . The first device of  claim 8 , wherein the first machine learning model is received based on a parameter that is associated with at least one of:
 scheduling,   an on-demand basis, or   a trigger.   
     
     
         10 . The first device of  claim 9 , wherein the first machine learning model is trained and updated by the second device. 
     
     
         11 . The first device of  claim 8 , wherein the predicted monetary amount comprises at least one of a predicted compensation amount or a predicted compensation date. 
     
     
         12 . The first device of  claim 8 , wherein additional information associated with a user of the first account is obtained from crawling one or more information sources that is not associated with the first account or the second account, and
 wherein the predicted reason is determined based on the additional information.   
     
     
         13 . The first device of  claim 8 , wherein the second machine learning model is trained based on historical additional information associated with a plurality of user accounts. 
     
     
         14 . The first device of  claim 8 , wherein the communication session is established based on causing the third device to send instructions to the fourth device for initiating the communication session. 
     
     
         15 . A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
 one or more instructions that, when executed by one or more processors of a first device, cause the first device to:
 receive from a second device, a first machine learning model,
 wherein the first machine learning model is trained based on at least a neural network technique that performs pattern recognition associated with historical information related to a monetary amount, and 
 wherein the monetary amount is associated with a first account; 
 
 process, using the first machine learning model, a monetary amount and employment information to determine a predicted monetary amount; 
 determine whether a new monetary amount associated with a second account is consistent with the predicted monetary amount; 
 determine, using a second machine learning model, a predicted reason for the new monetary amount not being consistent with the predicted monetary amount; and 
 establish a communication session between a third device, associated with the at least one of the first account or the second account, and a fourth device. 
   
     
     
         16 . The non-transitory computer-readable medium of  claim 15 , wherein the first machine learning model is received based on a parameter that is associated with at least one of:
 scheduling,   an on-demand basis, or   a trigger.   
     
     
         17 . The non-transitory computer-readable medium of  claim 16 , wherein the first machine learning model is trained and updated by the second device. 
     
     
         18 . The non-transitory computer-readable medium of  claim 15 , wherein additional information is obtained from crawling one or more information sources that is not associated with the first account or the second account, and
 wherein the predicted reason is determined based on the additional information.   
     
     
         19 . The non-transitory computer-readable medium of  claim 15 , wherein the second machine learning model is trained based on historical additional information associated with a plurality of user accounts. 
     
     
         20 . The non-transitory computer-readable medium of  claim 15 , wherein the communication session is established based on causing the third device to send instructions to the fourth device for initiating the communication session.