Predicting occurrences of targeted attrition events using trained artificial-intelligence processes

Abstract

The disclosed embodiments include computer-implemented systems and processes that predict occurrences of targeted attrition events using trained artificial-intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a targeted participant during a first temporal interval. Based on an application of a trained artificial-intelligence process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of an attrition event involving the targeted participant during a second temporal interval that is disposed subsequent to the first temporal interval, and that is separated from the first temporal interval by a buffer interval. The apparatus may transmit at least a portion of the generated output data and explainability data associated with the trained artificial-intelligence process to a computing system, which may perform operations based on the portion of the output data and the explainability data.

What is claimed is: 
     
         1 . An apparatus, comprising:
 a memory storing instructions;   a communications interface; and   at least one processor coupled to the memory and to the communications interface, the at least one processor being configured to execute the instructions to:
 generate an input dataset based on elements of first interaction data associated with a targeted participant in a service, the first interaction data being associated with a first temporal interval; 
 based on an application of a trained artificial intelligence process to the input dataset, generate output data representative of a predicted likelihood of an occurrence of an attrition event involving the targeted participant during a second temporal interval, the second temporal interval being subsequent to the first temporal interval and being separated from the first temporal interval by a corresponding buffer interval; and 
 transmit, to a computing system via the communications interface, at least a portion of the generated output data and elements of explainability data associated with the trained artificial intelligence process, the computing system being configured to perform operations based on the portion of the output data and the elements of explainability data. 
   
     
     
         2 . The apparatus of  claim 1 , wherein the at least one processor is further configured to execute the instructions to:
 receive at least a portion of the first interaction data from the computing system via the communications interface; and   store the received portion of the first interaction data within the memory.   
     
     
         3 . The apparatus of  claim 1 , wherein the at least one processor is further configured to execute the instructions to:
 obtain (i) one or more parameters that characterize the trained artificial intelligence process and (ii) data that characterizes a composition of the input dataset;   generate the input dataset in accordance with the data that characterizes the composition; and   apply the trained artificial intelligence process to the input dataset in accordance with the one or more parameters.   
     
     
         4 . The apparatus of  claim 1 , wherein the at least one processor is further configured to execute the instructions to:
 based on the data that characterizes the composition, perform operations that at least one of extract a first feature value from the first interaction data or compute a second feature value based on the first feature value; and   generate the input dataset based on at least one of the extracted first feature value or the computed second feature value.   
     
     
         5 . The apparatus of  claim 1 , wherein the trained artificial intelligence process comprises a trained, gradient-boosted, decision-tree process. 
     
     
         6 . The apparatus of  claim 1 , wherein the at least one processor is further configured to execute the instructions to:
 obtain elements of second interaction data, each of the elements of the second interaction data comprising a temporal identifier associated with a temporal interval;   based on the temporal identifiers, determine that a first subset of the elements of the second interaction data are associated with a prior training interval, and that a second subset of the elements of the second interaction data are associated with a prior validation interval;   generate a plurality of training datasets based on corresponding portions of the first subset; and   perform operations that train the artificial intelligence process based on the training datasets.   
     
     
         7 . The apparatus of  claim 6 , wherein the at least one processor is further configured to execute the instructions to:
 generate a plurality of validation datasets based on portions of the second subset;   apply the trained artificial intelligence process to the plurality of validation datasets, and generate additional elements of output data based on the application of the trained artificial intelligence process to the plurality of validation datasets;   compute one or more validation metrics based on the additional elements of output data; and   based on a determined consistency between the one or more validation metrics and a threshold condition, validate the trained artificial intelligence process.   
     
     
         8 . The apparatus of  claim 6 , wherein the at least one processor is further configured to execute instructions to perform operations that filter the elements of second interaction data in accordance with one or more targeted filtration criteria. 
     
     
         9 . The apparatus of  claim 1 , wherein:
 the input dataset comprises feature values associated with a plurality of input features; and   the at least one processor is further configured to execute the instructions to generate the explainability data associated with the trained artificial intelligence process, the explainability data comprising a feature contribution value characterizing a contribution of each of the feature values to the predicted likelihood of the occurrence of the attrition event during the second temporal interval.   
     
     
         10 . The apparatus of  claim 1 , wherein the output data comprise a numerical score indicative of the predicted likelihood of an occurrence of the attrition event during the second temporal interval. 
     
     
         11 . The apparatus of  claim 1 , wherein:
 the targeted participant is associated with a value of a parameter that exceeds a first threshold value; and   the occurrence of the attrition event involving the targeted participant during the second temporal interval is associated with a decrease in the parameter value that exceeds a second threshold value.   
     
     
         12 . The apparatus of  claim 1 , wherein the computing system is further configured to perform one or more treatment processes in accordance with the portion of the output data and the explainability data, the one or more treatment processes reducing the predicted likelihood of the occurrence of the attrition event during the second temporal interval. 
     
     
         13 . A computer-implemented method, comprising:
 generating, using at least one processor, an input dataset based on elements of first interaction data associated with a targeted participant in a service, the first interaction data being associated with a first temporal interval;   based on an application of a trained artificial intelligence process to the input dataset, generating, using the at least one processor, output data representative of a predicted likelihood of an occurrence of an attrition event involving the targeted participant during a second temporal interval, the second temporal interval being subsequent to the first temporal interval and being separated from the first temporal interval by a corresponding buffer interval; and   using the at least one processor, transmitting, to a computing system, at least a portion of the generated output data and elements of explainability data associated with the trained artificial intelligence process, the computing system being configured to perform operations based on the portion of the output data and the elements of explainability data.   
     
     
         14 . The computer-implemented method of  claim 13 , wherein:
 the computer-implemented method further comprises:
 using the at least one processor, obtaining (i) one or more parameters that characterize the trained artificial intelligence process and (ii) data that characterizes a composition of the input dataset; and 
 based on the data that characterizes the composition, performing operations, using the at least one processor, that at least one of extract a first feature value from the first interaction data or compute a second feature value based on the first feature value; 
   generating the input dataset comprises generating the input dataset based on at least one of the extracted first feature value or the computed second feature value; and   the computer-implemented method further comprises applying, using the at least one processor, the trained artificial intelligence process to the input dataset in accordance with the one or more parameters.   
     
     
         15 . The computer-implemented method of  claim 13 , wherein:
 the trained artificial intelligence process comprises a trained, gradient-boosted, decision-tree process; and   the output data comprises a numerical score indicative of the predicted likelihood of an occurrence of the attrition event during the second temporal interval.   
     
     
         16 . The computer-implemented method of  claim 13 , further comprising:
 obtaining, using the at least one processor, elements of second interaction data, each of the elements of the second interaction data comprising a temporal identifier associated with a temporal interval;   based on the temporal identifiers, determining, using the at least one processor, that a first subset of the elements of the second interaction data are associated with a prior training interval, that a second subset of the elements of the second interaction data are associated with a prior validation interval;   generating, using the at least one processor, a plurality of training datasets based on corresponding portions of the first subset; and   performing operations, using the at least one processor, that train the artificial intelligence process based on the training datasets.   
     
     
         17 . The computer-implemented method of  claim 16 , further comprising:
 generating, using the at least one processor, a plurality of validation datasets based on portions of the second subset;   using the at least one processor, applying the trained artificial intelligence process to the plurality of validation datasets, and generating additional elements of output data based on the application of the trained artificial intelligence process to the plurality of validation datasets;   computing, using the at least one processor, one or more validation metrics based on the additional elements of output data; and   based on a determined consistency between the one or more validation metrics and a threshold condition, validate the trained artificial intelligence process using the at least one processor.   
     
     
         18 . The computer-implemented method of  claim 13 , wherein:
 the input dataset comprises feature values associated with a plurality of input features; and   the computer-implemented method further comprises generating the explainability data associated with the trained artificial intelligence process using the at least one processor, the explainability data comprising a feature contribution value characterizing a contribution of each of the feature values to the predicted likelihood of the occurrence of the attrition event during the second temporal interval.   
     
     
         19 . The computer-implemented method of  claim 13 , wherein:
 the targeted participant is associated with a value of a parameter that exceeds a first threshold value; and   the occurrence of the attrition event involving the targeted participant during the second temporal interval is associated with a decrease in the parameter value that exceeds a second threshold value; and   the computing system is further configured to perform one or more treatment processes in accordance with the portion of the output data and the explainability data, the one or more treatment processes reducing the predicted likelihood of the occurrence of the attrition event during the second temporal interval.   
     
     
         20 . A tangible, non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform a method, comprising:
 generating an input dataset based on elements of first interaction data associated with a targeted participant in a service, the first interaction data being associated with a first temporal interval;   based on an application of a trained artificial intelligence process to the input dataset, generating output data representative of a predicted likelihood of an occurrence of an attrition event involving the targeted participant during a second temporal interval, the second temporal interval being subsequent to the first temporal interval and being separated from the first temporal interval by a corresponding buffer interval; and   transmitting, to a computing system, at least a portion of the generated output data and elements of explainability data associated with the trained artificial intelligence process, the computing system being configured to perform operations based on the portion of the output data and the elements of explainability data.