Abstract: A system and method for quantile-based assessment and handling of digital events in a digital threat mitigation platform includes receiving, via an application programming interface (API), a request from a subscriber to assess a threat of a digital event, computing, using one or more threat scoring machine learning models, a digital threat inference based on one or more corpora of feature vectors associated with the digital event, wherein the digital threat inference includes an uncalibrated digital threat score, retrieving, from a database, a T-Digest data structure of historical digital threat scores of the subscriber, computing, using the T-Digest data structure of historical digital threat scores, a percentile-based threat score based on the uncalibrated digital threat score computed for the digital event, and executing an automated disposal decision computed for the digital event based on at least the percentile-based threat score satisfying automated decisioning instructions of the digital threat mitiga

Abstract: A system and method for quantile-based assessment and handling of digital events in a digital threat mitigation platform includes receiving, via an application programming interface (API), a request from a subscriber to assess a threat of a digital event, computing, using one or more threat scoring machine learning models, a digital threat inference based on one or more corpora of feature vectors associated with the digital event, wherein the digital threat inference includes an uncalibrated digital threat score, retrieving, from a database, a T-Digest data structure of historical digital threat scores of the subscriber, computing, using the T-Digest data structure of historical digital threat scores, a percentile-based threat score based on the uncalibrated digital threat score computed for the digital event, and executing an automated disposal decision computed for the digital event based on at least the percentile-based threat score satisfying automated decisioning instructions of the digital threat mitiga

Abstract: A system and method for quantile-based assessment and handling of digital events in a digital threat mitigation platform includes receiving, via an application programming interface (API), a request from a subscriber to assess a threat of a digital event, computing, using one or more threat scoring machine learning models, a digital threat inference based on one or more corpora of feature vectors associated with the digital event, wherein the digital threat inference includes an uncalibrated digital threat score, retrieving, from a database, a T-Digest data structure of historical digital threat scores of the subscriber, computing, using the T-Digest data structure of historical digital threat scores, a percentile-based threat score based on the uncalibrated digital threat score computed for the digital event, and executing an automated disposal decision computed for the digital event based on at least the percentile-based threat score satisfying automated decisioning instructions of the digital threat mitiga

Abstract: A system and method for quantile-based assessment and handling of digital events in a digital threat mitigation platform includes receiving, via an application programming interface (API), a request from a subscriber to assess a threat of a digital event, computing, using one or more threat scoring machine learning models, a digital threat inference based on one or more corpora of feature vectors associated with the digital event, wherein the digital threat inference includes an uncalibrated digital threat score, retrieving, from a database, a T-Digest data structure of historical digital threat scores of the subscriber, computing, using the T-Digest data structure of historical digital threat scores, a percentile-based threat score based on the uncalibrated digital threat score computed for the digital event, and executing an automated disposal decision computed for the digital event based on at least the percentile-based threat score satisfying automated decisioning instructions of the digital threat mitiga

Abstract: A system and method for accelerating a disposition of a digital dispute event includes routing a digital dispute event to one of a plurality of distinct machine learning-based dispute scoring models; computing, by the one of the plurality of distinct machine learning-based dispute scoring models, a preliminary machine learning-based dispute inference based on one or more features extracted from the digital dispute event, wherein the preliminary machine learning-based dispute inference relates to a probability of the subscriber prevailing against the digital dispute event based on each piece of evidence data of a service-proposed corpus of evidence data being available to include in a dispute response artifact; and generating the dispute response artifact based on the digital dispute event, wherein the generating includes installing one or more obtainable pieces of evidence data associated with the digital event into one or more distinct sections of the dispute response artifact.

Abstract: A method for machine learning-based detection of an automated fraud or abuse attack includes: identifying, via a computer network, a digital event associated with a suspected automated fraud or abuse attack; composing, via one or more computers, a digital activity signature of the suspected automated fraud or abuse attack based on digital activity associated with the suspected automated fraud or abuse attack; computing, via a machine learning model, an encoded representation of the digital activity signature; searching, via the one or more computers, an automated fraud or abuse signature registry based on the encoded representation of the digital activity signature; determining a likely origin of the digital event based on the searching of the automated fraud or abuse signature registry; and selectively implementing one or more automated threat mitigation actions based on the likely origin of the digital event.

Abstract: A system and method for accelerating a disposition of a digital dispute event includes routing a digital dispute event to one of a plurality of distinct machine learning-based dispute scoring models; computing, by the one of the plurality of distinct machine learning-based dispute scoring models, a preliminary machine learning-based dispute inference based on one or more features extracted from the digital dispute event, wherein the preliminary machine learning-based dispute inference relates to a probability of the subscriber prevailing against the digital dispute event based on each piece of evidence data of a service-proposed corpus of evidence data being available to include in a dispute response artifact; and generating the dispute response artifact based on the digital dispute event, wherein the generating includes installing one or more obtainable pieces of evidence data associated with the digital event into one or more distinct sections of the dispute response artifact.

Abstract: A machine learning-based method for accelerating a generation of automated fraud or abuse detection workflows in a digital threat mitigation platform includes identifying a plurality of distinct digital event features indicative of digital fraud; automatically deriving a plurality of distinct digital event decisioning criteria based on the plurality of distinct digital event features and a digital event data corpus associated with a target subscriber; automatically constructing a probationary automated fraud or abuse detection workflow based on the plurality of distinct digital event decisioning criteria, and deploying the probationary automated fraud or abuse detection workflow to a target digital fraud prevention environment associated with the target subscriber.

Abstract: A system and method for accelerated anomaly detection and replacement of an anomaly-experiencing machine learning-based ensemble includes identifying a machine learning-based digital threat scoring ensemble having an anomalous drift behavior in digital threat score inferences computed by the machine learning-based digital threat scoring ensemble for a target period; executing a tiered anomaly evaluation for the machine learning-based digital threat scoring ensemble that includes identifying at least one errant machine learning-based model of the machine learning-based digital threat scoring ensemble contributing to the anomalous drift behavior, and identifying at least one errant feature variable of the at least one machine learning-based model contributing to the anomalous drift behavior; generating a successor machine learning-based digital threat scoring ensemble to the machine learning-based digital threat scoring ensemble based on the tiered anomaly evaluation; and replacing the machine learning-based di

Abstract: A method for machine learning-based detection of an automated fraud or abuse attack includes: identifying, via a computer network, a digital event associated with a suspected automated fraud or abuse attack; composing, via one or more computers, a digital activity signature of the suspected automated fraud or abuse attack based on digital activity associated with the suspected automated fraud or abuse attack; computing, via a machine learning model, an encoded representation of the digital activity signature; searching, via the one or more computers, an automated fraud or abuse signature registry based on the encoded representation of the digital activity signature; determining a likely origin of the digital event based on the searching of the automated fraud or abuse signature registry; and selectively implementing one or more automated threat mitigation actions based on the likely origin of the digital event.

Abstract: A system and method for accelerated anomaly detection and replacement of an anomaly-experiencing machine learning-based ensemble includes identifying a machine learning-based digital threat scoring ensemble having an anomalous drift behavior in digital threat score inferences computed by the machine learning-based digital threat scoring ensemble for a target period; executing a tiered anomaly evaluation for the machine learning-based digital threat scoring ensemble that includes identifying at least one errant machine learning-based model of the machine learning-based digital threat scoring ensemble contributing to the anomalous drift behavior, and identifying at least one errant feature variable of the at least one machine learning-based model contributing to the anomalous drift behavior; generating a successor machine learning-based digital threat scoring ensemble to the machine learning-based digital threat scoring ensemble based on the tiered anomaly evaluation; and replacing the machine learning-based di

Abstract: A system and method for accelerating an automated labeling of a volume of unlabeled digital event data samples includes identifying a corpus characteristic of a digital event data corpus that includes a plurality of distinct unlabeled digital event data samples; selecting an automated bulk labeling algorithm based on the corpus characteristic associated with the digital event data corpus satisfying a bulk labeling criterion of the automated bulk labeling algorithm; evaluating a subset of the plurality of unlabeled digital event data samples, wherein evaluating the subset includes attributing a distinct classification label to each digital event data sample within the subset; and in response to the selection, executing the selected automated bulk labeling algorithm against the digital event data corpus, wherein the executing includes simultaneously assigning a classification label equivalent to the distinct classification label to a superset of the digital event data corpus that relates to the subset.

Abstract: Systems and methods for detecting digital abuse or digital fraud that involves malicious account testing includes implementing a machine learning threat model that predicts malicious account testing using misappropriate accounts, wherein a subset of a plurality of learnable variables of an algorithmic structure of the machine learning threat model includes one or more learnable variables derived based on feature data indicative of malicious account testing; wherein implementing the machine learning threat model includes: (i) identifying event data from an online event that is suspected to involve digital fraud or digital abuse, (ii) extracting adverse feature data from the event data that map to the one or more learnable variables of the subset, and (iii) providing the adverse feature data as model input to the machine learning threat model; and computing, using the machine learning threat model, a threat prediction indicating a probability that the online event involves malicious account testing.

Abstract: A system and method for adapting an errant automated decisioning workflow includes reconfiguring digital abuse or digital fraud logic parameters associated with automated decisioning routes of an automated decisioning workflow in response to identifying an anomalous drift or an anomalous shift in efficacy metrics of the automated decisioning workflow, wherein the automated decisioning workflow includes a plurality of distinct automated decisioning routes that, when applied in a digital threat evaluation of data associated with a target digital event, automatically compute a decision for disposing the target digital event based on a probability digital fraud; simulating, by computers, a performance of the automated decisioning routes in a reconfigured state based on inputs of historical digital event data; calculating simulation metrics based on simulation output data of the simulation; and promoting to an in-production state the automated decisioning workflow having the automated decisioning routes in the rec

Abstract: A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse/digital fraud and not digital abuse/digital fraud; and identifying members or content clusters having digital fraud/digital abuse based on querying the distinct clusters of content data.

Abstract: A system and method for adaptively sampling a corpus of data samples for improving an accuracy of a predictive machine learning model includes: identifying the corpus of data samples, wherein each data sample of the corpus of data samples is associated with a machine learning-derived threat inference value; stratifying the corpus of data samples into a plurality of distinct strata based on the machine learning-derived threat inference value associated with each data sample of the corpus of data samples; adaptively sampling the plurality of distinct strata; constructing a machine learning training corpus comprising a plurality of data samples based on the adaptive sampling of the plurality of distinct strata; and training the predictive machine learning model based on the machine learning training corpus.

Abstract: A system and method for accelerating an automated labeling of a volume of unlabeled digital event data samples includes identifying a corpus characteristic of a digital event data corpus that includes a plurality of distinct unlabeled digital event data samples; selecting an automated bulk labeling algorithm based on the corpus characteristic associated with the digital event data corpus satisfying a bulk labeling criterion of the automated bulk labeling algorithm; evaluating a subset of the plurality of unlabeled digital event data samples, wherein evaluating the subset includes attributing a distinct classification label to each digital event data sample within the subset; and in response to the selection, executing the selected automated bulk labeling algorithm against the digital event data corpus, wherein the executing includes simultaneously assigning a classification label equivalent to the distinct classification label to a superset of the digital event data corpus that relates to the subset.

Abstract: Systems and methods for detecting digital abuse or digital fraud that involves malicious account testing includes implementing a machine learning threat model that predicts malicious account testing using misappropriate accounts, wherein a subset of a plurality of learnable variables of an algorithmic structure of the machine learning threat model includes one or more learnable variables derived based on feature data indicative of malicious account testing; wherein implementing the machine learning threat model includes: (i) identifying event data from an online event that is suspected to involve digital fraud or digital abuse, (ii) extracting adverse feature data from the event data that map to the one or more learnable variables of the subset, and (iii) providing the adverse feature data as model input to the machine learning threat model; and computing, using the machine learning threat model, a threat prediction indicating a probability that the online event involves malicious account testing.

Abstract: A system and method for adapting an errant automated decisioning workflow includes reconfiguring digital abuse or digital fraud logic parameters associated with automated decisioning routes of an automated decisioning workflow in response to identifying an anomalous drift or an anomalous shift in efficacy metrics of the automated decisioning workflow, wherein the automated decisioning workflow includes a plurality of distinct automated decisioning routes that, when applied in a digital threat evaluation of data associated with a target digital event, automatically compute a decision for disposing the target digital event based on a probability digital fraud; simulating, by computers, a performance of the automated decisioning routes in a reconfigured state based on inputs of historical digital event data; calculating simulation metrics based on simulation output data of the simulation; and promoting to an in-production state the automated decisioning workflow having the automated decisioning routes in the rec

Abstract: A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse/digital fraud and not digital abuse/digital fraud; and identifying members or content clusters having digital fraud/digital abuse based on querying the distinct clusters of content data.