SYSTEM AND METHOD FOR ASSESSING A DIGITAL INTERACTION WITH A DIGITAL THIRD PARTY ACCOUNT SERVICE

1 - 20 . (canceled)

21 . A method comprising:

at an operating computing platform, storing user account credentials for a user account of an external account service in association with authentication data, and communicating the authentication data to a computing device of an application service;

receiving, through a communication interface of the operating computing platform, a request referencing the authentication data and specifying details of a digital interaction;

authenticating, using the user account credentials, the user account with the external account service and retrieving account data therefrom;

generating a score for the digital interaction using a machine learning model, wherein the score represents a measure of risk or confidence associated with the digital interaction, wherein the machine learning model is trained on interaction outcome data accumulated across a network of users spanning a plurality of application services integrated with the operating computing platform, and wherein the machine learning model receives as input a least:

behavioral data derived from usage of the user account across two or more application services of the plurality of application services;

device-associated data derived from one or more prior digital interactions of the user account; and

account-level signals comprising at least account identity information, account balance state, and transaction history retrieved from the external account service;

transmitting the score to the application service via the communication interface; and

controlling execution of the digital interaction based at least in part on the score.

22 . The method of claim 21 , wherein the behavioral data is based on a record maintained by the operating computing platform and comprises a count of distinct application services to which the user account has been linked.

23 . The method of claim 21 , further comprising:

receiving, from the application service, outcome data indicating a result of the digital interaction; and

updating trained parameters of the machine learning model based on the outcome data, wherein the updated trained parameters are applied in generating one or more scores for subsequent digital interactions.

24 . The method of claim 21 , further comprising:

generating a first risk sub-score reflecting a probability of a funding-related return;

generating a second risk sub-score reflecting a probability of an unauthorized transaction return; and

wherein the score is based at least in part on the first risk sub-score and the second risk sub-score.

25 . The method of claim 21 , wherein for a particular user account having no prior interaction history within the operating computing platform, generating the score comprises deriving initial feature inputs by matching one or more attributes of the user account to historical interaction patterns accumulated from the network of users, thereby providing an immediately actionable score based on network-derived context.

26 . The method of claim 21 , wherein retrieving the account data further comprises accessing a second user account associated with a same user and retrieving second account data therefrom, and wherein generating the score further comprises incorporating second account-level signals derived from the second user account as feature inputs to the machine learning model.

27 . The method of claim 21 , wherein controlling the execution of the digital interaction comprises expediting processing of the digital interaction by reducing or eliminating a waiting period otherwise applicable to the digital interaction when the score satisfies a low-risk threshold.

28 . The method of claim 21 , wherein generating the score further comprises identifying, based on the device-associated data and as an identification, whether a device associated with the digital interaction has been previously associated with the user account or with one or more other user accounts within the network of users, and incorporating the identification as a feature input to the machine learning model.

29 . A system comprising:

one or more processors; and

one or more non-transitory computer-readable media storing computer executable instructions that, when executed, cause the one or more processors to perform operations comprising:

storing user account credentials for a user account of an external account service in association with authentication data, and communicating the authentication data to a computing device of an application service;

receiving, through a communication interface, a request referencing the authentication data and specifying details of a digital interaction;

authenticating, using the user account credentials, the user account with the external account service and retrieving account data therefrom;

generating a score for the digital interaction using a machine learning model, wherein the score represents a measure of risk or confidence associated with the digital interaction, wherein the machine learning model is trained on interaction outcome data accumulated across a network of users spanning a plurality of application services integrated with the system, and wherein the machine learning model receives as input at least:

behavioral data derived from usage of the user account across two or more application services of the plurality of application services;

device-associated data derived from one or more prior digital interactions of the user account; and

account-level signals comprising at least account identity information, account balance state, and transaction history retrieved from the external account service;

transmitting the score to the application service via the communication interface; and

controlling execution of the digital interaction based at least in part on the score.

30 . The system of claim 29 , wherein the behavioral data is based on a record and comprises a count of distinct application services to which the user account has been linked.

31 . The system of claim 29 , wherein the operations further comprise:

receiving, from the application service, outcome data indicating a result of the digital interaction; and

updating trained parameters of the machine learning model based on the outcome data, wherein the updated trained parameters are applied in generating one or more scores for subsequent digital interactions.

32 . The system of claim 29 , wherein the operations further comprise:

generating a first risk sub-score reflecting a probability of a funding-related return;

generating a second risk sub-score reflecting a probability of an unauthorized transaction return; and

wherein the score is based at least in part on the first risk sub-score and the second risk sub-score.

33 . The system of claim 29 , wherein for a particular user account having no prior interaction history within the system, generating the score comprises deriving initial feature inputs by matching one or more attributes of the user account to historical interaction patterns accumulated from the network of users, thereby providing an immediately actionable score based on network-derived context.

34 . The system of claim 29 , wherein retrieving the account data further comprises accessing a second user account associated with a same user and retrieving second account data therefrom, and wherein generating the score further comprises incorporating second account-level signals derived from the second user account as feature inputs to the machine learning model.

35 . The system of claim 29 , wherein controlling the execution of the digital interaction comprises expediting processing of the digital interaction by reducing or eliminating a waiting period otherwise applicable to the digital interaction when the score satisfies a low-risk threshold.

36 . The system of claim 29 , wherein generating the score further comprises identifying, based on the device-associated data and as an identification, whether a device associated with the digital interaction has been previously associated with the user account or with one or more other user accounts within the network of users, and incorporating the identification as a feature input to the machine learning model.

37 . One or more non-transitory computer-readable media storing computer executable instructions that, when executed, cause one or more processors to perform operations comprising:

storing user account credentials for a user account of an external account service in association with authentication data, and communicating the authentication data to a computing device of an application service;

receiving, through a communication interface, a request referencing the authentication data and specifying details of a digital interaction;

authenticating, using the user account credentials, the user account with the external account service and retrieving account data therefrom;

generating a score for the digital interaction using a machine learning model, wherein the score represents a measure of risk or confidence associated with the digital interaction, wherein the machine learning model is trained on interaction outcome data accumulated across a network of users spanning a plurality of application services integrated with an operating computing platform, and wherein the machine learning model receives as input at least:

behavioral data derived from usage of the user account across two or more application services of the plurality of application services;

device-associated data derived from one or more prior digital interactions of the user account; and

account-level signals comprising at least account identity information, account balance state, and transaction history retrieved from the external account service;

transmitting the score to the application service via the communication interface; and

controlling execution of the digital interaction based at least in part on the score.

38 . The one or more non-transitory computer-readable media of claim 37 , wherein the behavioral data is based on a record maintained by the operating computing platform and comprises a count of distinct application services to which the user account has been linked.

39 . The one or more non-transitory computer-readable media of claim 37 , wherein the operations further comprise:

receiving, from the application service, outcome data indicating a result of the digital interaction; and

updating trained parameters of the machine learning model based on the outcome data, wherein the updated trained parameters are applied in generating one or more scores for subsequent digital interactions.

40 . The one or more non-transitory computer-readable media of claim 37 , wherein the operations further comprise:

generating a first risk sub-score reflecting a probability of a funding-related return;

generating a second risk sub-score reflecting a probability of an unauthorized transaction return; and

wherein the score is based at least in part on the first risk sub-score and the second risk sub-score.