Granted Patent B2

US 12,651,205 · App. 17/714,275 · Granted Jun 9, 2026

Inventors: Harry Joseph Braviner (Toronto, CA); Maksims Volkovs (Toronto, CA); Tomi Johan Poutanen (Toronto, CA)

Assignee: The Toronto-Dominion Bank

G06N20/20G06Q40/06

Abstract

Claims (81)

1 . An apparatus, comprising:

a memory storing instructions;

a communications interface; and

at least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to:

receive, via the communications interface, an identifier associated with a customer from a computing system, and based on the received identifier, obtain, from the memory, elements of first interaction data associated with a first temporal interval and with the received identifier, the elements of first interaction data comprising an element of engagement data characterizing an engagement of the customer during the first temporal interval;

generate an input dataset based on the elements of first interaction data associated with the 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 associated with the customer and the engagement 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 at least a portion of the generated output data to a computing system via the communications interface, the computing system being configured to perform operations, based on the portion of the output data, that reduce the predicted likelihood of the occurrence of the attrition event during the second temporal interval.

2 . The apparatus of claim 1 , wherein the at least one processor is further configured 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:

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:

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 element of engagement data; 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;

the output data comprise a numerical score indicative of the predicted likelihood of an occurrence of the attrition event during the second temporal interval.

6 . The apparatus of claim 1 , wherein the at least one processor is further configured to execute instructions to:

perform operations that filter the first interaction data in accordance with one or more filtration criteria; and

generate the input dataset based on at least a portion of the filtered first interaction data.

7 . 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, the one or more treatment processes reducing the predicted likelihood of the occurrence of the attrition event during the second temporal interval.

8 . 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 and the targeting data.

9 . The apparatus of claim 8 , 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.

10 . The apparatus of claim 1 , wherein:

the input dataset comprises feature values associated with a plurality of input features;

the at least one processor is further configured to execute the instructions to:

generate 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; and

transmit at least the portion of the output data and the explainability data to the computing system via the communications interface, the computing system being configured to perform the operations based on the portion of the output data and the explainability data.

11 . The apparatus of claim 1 , wherein:

the engagement involves a planning service provisioned to the customer, and the attrition event is associated with the customer and the planning service provisioned to the customer; and

the element of engagement data comprises at least one of (i) a distance between a first geographic location associated with the customer and a second geographic location associated with the planning service or (ii) a temporal displacement between a current time and a prior engagement of the customer and the planning service.

12 . A computer-implemented method, comprising:

receiving, using at least one processor, an identifier associated with a customer from a computing system, and based on the received identifier, obtaining, using the at least one processor, elements of first interaction data associated with a first temporal interval and with the received identifier from a data repository, the elements of first interaction data comprising an element of engagement data characterizing an engagement of the customer during the first temporal interval;

generating, using the at least one processor, an input dataset based on the elements of first interaction data associated with the 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 associated with the customer and the engagement 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, using the at least one processor, at least a portion of the generated output data to a computing system, the computing system being configured to perform operations, based on the portion of the output data, that reduce the predicted likelihood of the occurrence of the attrition event during the second temporal interval.

13 . The computer-implemented method of claim 12 , wherein:

the computer-implemented method further comprises obtaining, using the at least one processor, (i) one or more parameters that characterize the trained artificial intelligence process and (ii) data that characterizes a composition of the input dataset;

generating the input dataset comprises generating the input dataset in accordance with the data that characterizes the composition; 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.

14 . The computer-implemented method of claim 12 , wherein:

the computer-implemented method further comprises, 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 element of engagement data; and

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.

15 . The computer-implemented method of claim 12 , wherein:

the trained artificial intelligence process comprises a trained, gradient-boosted, decision-tree process;

the output data comprise a numerical score indicative of the predicted likelihood of an occurrence of the attrition event during the second temporal interval;

the computing system is further configured to perform one or more treatment processes in accordance with the portion of the output data, the one or more treatment processes reducing the predicted likelihood of the occurrence of the attrition event during the second temporal interval.

16 . The computer-implemented method of claim 12 , 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, and 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 and the targeting data.

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, validating the trained artificial intelligence process using the at least one processor.

18 . The computer-implemented method of claim 12 , wherein:

the input dataset comprises feature values associated with a plurality of input features;

the computer-implemented method further comprises generating, using the at least one processor, explainability data associated with the trained artificial intelligence process, the explainability data comprising a feature contribution value characterizing a contribution of each of the input feature values to the predicted likelihood of the occurrences of the targeted events during the second temporal interval; and

the transmitting comprises transmitting at least the portion of the output data and the explainability data to the computing system, the computing system being configured to perform the operations based on the portion of the output data and the explainability data.

19 . 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:

receiving an identifier associated with a customer from a computing system, and based on the received identifier, obtaining, from a data repository, elements of first interaction data associated with a first temporal interval and with the received identifier, the elements of first interaction data comprising an element of engagement data characterizing an engagement of the customer during the first temporal interval;

generating an input dataset based on the elements of first interaction data associated with the 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 associated with the customer and the engagement 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 at least a portion of the generated output data to a computing system, the computing system being configured to perform operations, based on the portion of the output data, that reduce the predicted likelihood of the occurrence of the attrition event during the second temporal interval.

20 . The apparatus of claim 1 , wherein:

the input dataset comprises a plurality of feature values; and

the plurality of feature values comprise at least one of a value of a geographic parameter of the engagement or a value of a temporal value of the engagement.

Assignments (1)

Continuity (2)

References Cited (13)