Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a machine learning model to determine predicted client intent classifications and generate personalized digital text reply options within an automated interactive digital text thread. For example, disclosed systems utilize the machine learning model to generate predicted client intent classifications and corresponding intent classification probabilities. The disclosed systems utilize the predicted client disposition classifications and the disposition classification probabilities to generate personalized digital text reply options. Moreover, the disclosed systems can provide personalized digital text reply options to a client device within an automated interactive digital text thread, bypassing the inefficiency of menu options or protocols utilized to guide clients to terminal information.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to determine predicted client intent classifications and/or client-agent escalation classes to generate personalized digital text reply options within an automated interactive digital text thread. For example, disclosed systems utilize the machine learning model to generate predicted client-agent escalation classes and corresponding probabilities. The disclosed systems utilize the predicted client-agent escalation classifications and the escalation class probabilities to generate personalized digital text reply options. Moreover, the disclosed systems can provide personalized digital text reply options to a client device within an automated interactive digital text thread, bypassing the inefficiency of menu options or protocols utilized to guide clients to terminal information.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a machine learning model to determine predicted client intent classifications and generate personalized digital text reply options within an automated interactive digital text thread. For example, disclosed systems utilize the machine learning model to generate predicted client intent classifications and corresponding intent classification probabilities. The disclosed systems utilize the predicted client disposition classifications and the disposition classification probabilities to generate personalized digital text reply options. Moreover, the disclosed systems can provide personalized digital text reply options to a client device within an automated interactive digital text thread, bypassing the inefficiency of menu options or protocols utilized to guide clients to terminal information.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to determine predicted client intent classifications and/or client-agent escalation classes to generate personalized digital text reply options within an automated interactive digital text thread. For example, disclosed systems utilize the machine learning model to generate predicted client-agent escalation classes and corresponding probabilities. The disclosed systems utilize the predicted client-agent escalation classifications and the escalation class probabilities to generate personalized digital text reply options. Moreover, the disclosed systems can provide personalized digital text reply options to a client device within an automated interactive digital text thread, bypassing the inefficiency of menu options or protocols utilized to guide clients to terminal information.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a machine learning model to determine predicted client intent classifications and generate personalized digital text reply options within an automated interactive digital text thread. For example, disclosed systems utilize the machine learning model to generate predicted client intent classifications and corresponding intent classification probabilities. The disclosed systems utilize the predicted client disposition classifications and the disposition classification probabilities to generate personalized digital text reply options. Moreover, the disclosed systems can provide personalized digital text reply options to a client device within an automated interactive digital text thread, bypassing the inefficiency of menu options or protocols utilized to guide clients to terminal information.

Abstract: A system and method for generating an insult rate and reconfiguring an automated decisioning workflow includes configuring a testing group based on sampling from online events having an adverse disposal decision computed by an automated decisioning workflow computer that is configured with machine learning-based threat score thresholds that, if satisfied, causes a computation of a disallow decision or a block decision for a given online event; evaluating a performance and collecting performance data of distinct members of the testing group over a testing period; computing an insult rate for the testing group based on the performance data; computing an insult rate equilibrium for the automated decisioning workflow computer based on the performance data; evaluating the insult rate against the insult rate equilibrium; and reconfiguring adverse decisioning thresholds based on the evaluation of the insult rate of the testing group against the insult rate equilibrium for the automated decisioning workflow computer.

Abstract: A system and method for generating an insult rate and reconfiguring an automated decisioning workflow includes configuring a testing group based on sampling from online events having an adverse disposal decision computed by an automated decisioning workflow computer that is configured with machine learning-based threat score thresholds that, if satisfied, causes a computation of a disallow decision or a block decision for a given online event; evaluating a performance and collecting performance data of distinct members of the testing group over a testing period; computing an insult rate for the testing group based on the performance data; computing an insult rate equilibrium for the automated decisioning workflow computer based on the performance data; evaluating the insult rate against the insult rate equilibrium; and reconfiguring adverse decisioning thresholds based on the evaluation of the insult rate of the testing group against the insult rate equilibrium for the automated decisioning workflow computer.

Abstract: A system and method for generating an insult rate and reconfiguring an automated decisioning workflow includes configuring a testing group based on sampling from online events having an adverse disposal decision computed by an automated decisioning workflow computer that is configured with machine learning-based threat score thresholds that, if satisfied, causes a computation of a disallow decision or a block decision for a given online event; evaluating a performance and collecting performance data of distinct members of the testing group over a testing period; computing an insult rate for the testing group based on the performance data; computing an insult rate equilibrium for the automated decisioning workflow computer based on the performance data; evaluating the insult rate against the insult rate equilibrium; and reconfiguring adverse decisioning thresholds based on the evaluation of the insult rate of the testing group against the insult rate equilibrium for the automated decisioning workflow computer.