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公开(公告)号：US20230085575A1
公开(公告)日：2023-03-16
申请号：US17473687
申请日：2021-09-13
IPC分类号： G06N3/04
摘要： To eliminating bias from artificial intelligent (AI) systems, a list of class identifiers and features derived from class identifiers represented in training data fed to an AI system are identified for purpose of training a predictive model. Correlation analysis of input features is conducted from a list of raw variables, r, in a dataset and a plurality of derived features, x, with one or more class identifiers in the list of class identifiers and features derived from these class identifiers. A first list of input features is identified, one or more input features are in the first list belonging to and correlated with the one or more class identifiers or features derived from class identifiers. A second list of sets of input features is created to identify a set of combinations of input features that are not allowed to interact based on identifying biased latent features.
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公开(公告)号：US11468472B2
公开(公告)日：2022-10-11
申请号：US15442434
申请日：2017-02-24
IPC分类号： G06Q30/02
摘要： A system and method for scalable, adaptive, real-time generation of personalized offers is disclosed. A profile of a user is generated, the profile being a summarized representation of historical behavior of the user, the profile containing recursively updated variables. The profile is updated for each new transaction and/or a time dependent event, the new transaction and/or time dependent event including purchase transaction data, user, item hierarchy, and offer data. A affinity scores is generated for the user based on the updated profile, and for each new transaction, one or more offers are generated for the user based on the updated user profile and the affinity scores.
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公开(公告)号：US11423414B2
公开(公告)日：2022-08-23
申请号：US15074977
申请日：2016-03-18
IPC分类号： G06Q30/00 , G06Q20/40 , G06Q50/26 , H04L67/306 , G06N20/00 , G06F16/9535 , G06F16/2457 , H04L67/50
摘要： An automated system for detecting risky entity behavior using an efficient frequent behavior-sorted list is disclosed. From these lists, fingerprints and distance measures can be constructed to enable comparison to known risky entities. The lists also facilitate efficient linking of entities to each other, such that risk information propagates through entity associations. These behavior sorted lists, in combination with other profiling techniques, which efficiently summarize information about the entity within a data store, can be used to create threat scores. These threat scores may be applied within the context of anti-money laundering (AML) and retail banking fraud detection systems. A particular instantiation of these scores elaborated here is the AML Threat Score, which is trained to identify behavior for a banking customer that is suspicious and indicates high likelihood of money laundering activity.
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公开(公告)号：US11354292B2
公开(公告)日：2022-06-07
申请号：US15981755
申请日：2018-05-16
发明人： Scott Michael Zoldi , Shafi Rahman
摘要： A system and method for analyzing coverage, bias and model explanations in large dimensional modeling data includes discretizing three or more variables of a dataset to generate a discretized phase space represented as a grid of a plurality of cells, the dataset comprising a plurality of records, each record of the plurality of records having a value and a unique identifier (ID). A grid transformation is applied to each record in the dataset to assign each record to a cell of the plurality of cells of the grid according to the grid transformation. A grid index is generated to reference each cell using a discretized feature vector. A grid storage for storing the records assigned to each cell of the grid is then created. The grid storage using the ID of each record as a reference to each record and the discretized feature vector as a key to each cell.
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公开(公告)号：US20220166782A1
公开(公告)日：2022-05-26
申请号：US17102295
申请日：2020-11-23
摘要： Systems, methods and computer program products for improving security of artificial intelligence systems. The system comprising processors for monitoring one or more transactions received by a machine learning decision model to determine a first score associated with a first transaction. The first transaction may be identified as likely adversarial, in response to the first score being lower than a certain score threshold and the first transaction having a low occurrence likelihood. A second score may be generated in association with the first transaction based on one or more adversarial latent features associated with the first transaction. At least one adversarial latent feature may be detected as being exploited by the first transaction, in response to determining that the second score falls above the certain score threshold. Accordingly, an abnormal volume of activations of adversarial latent features spanning across a plurality of transactions scored may be detected and blocked.
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公开(公告)号：US20210406703A1
公开(公告)日：2021-12-30
申请号：US17470951
申请日：2021-09-09
发明人： Scott Michael Zoldi , Jim Coggeshall , Yuting Jia
摘要： A predictive analytics system and method in the setting of multi-class classification are disclosed, for identifying systematic changes in an evaluation dataset processed by a fraud-detection model by examining the time series histories of an ensemble of entities such as accounts. The ensemble of entities is examined and processed both individually and in aggregate, via a set of features determined previously using a distinct training dataset. The specific set of features in question may be calculated from the entity's time series history, and may or may not be used by the model to perform the classification. Certain properties of the detected changes are measured and used to improve the efficacy of the predictive model.
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7.发明授权公开(公告)号：US10896381B2
公开(公告)日：2021-01-19
申请号：US15074856
申请日：2016-03-18
IPC分类号： G06Q10/00 , G06F15/16 , G06F16/33 , G06Q30/02 , G06N20/00 , G06Q30/06 , G06Q10/10 , G06N7/00
摘要： An automated way of learning archetypes which capture many aspects of entity behavior, and assigning entities to a mixture of archetypes, such that each entity is represented as a distribution across multiple archetypes. Given those representations in archetypes, anomalous behavior can be detected by finding misalignment with a plurality of entities archetype clustering within a hard segmentation. Extensions to sequence modeling are also discussed. Applications of this method include anti-money laundering (where the entities can be customers and accounts, as described extensively below), retail banking fraud detection, network security, and general anomaly detection.
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公开(公告)号：US20210004703A1
公开(公告)日：2021-01-07
申请号：US16460934
申请日：2019-07-02
摘要： In transactional systems where past transactions can have impact on the current score of a machine learning based decision model, the transactions that are most responsible for the score and the associated reasons are determined by the transactional system. A system and method identifies such past transactions that maximally impact the current score and allow for a more effective understanding of the scores generated by a model in a transactional system and explanation of specific transactions for automated decisioning, to explain the scores in terms of past transactions. Further an existing instance-based explanation system is used to identify the reasons for the score, and how the identified transactions influence these reasons. A combination of impact on score and impact on reasons determines the most impactful past transaction with respect to the most recent score being explained.
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公开(公告)号：US20200082302A1
公开(公告)日：2020-03-12
申请号：US16128359
申请日：2018-09-11
发明人： Scott Michael Zoldi , Shafi Rahman
摘要： A model governance framework uses a shared ledger on the back of a blockchain. The solution tracks various analytic tracking documents (ATDs) and associated assets, like requirements and sprints, through various stages of an ATD lifecycle. Data schema and data distributions are also tracked. The decision models, corresponding variables and execution codes are also tracked. Existing variables and execution codes are made available via a preexisting asset ATD for reuse. Various transactions provide mechanism for accessing and manipulating the various assets through a recorded ledger of events and approvals. A system provides tracking of the approvals and the approvers of all model assets that are touched by any participant, and further provides access control and security for multi-user access. An application layer provides graphical access to the various aspects of the blockchain. Event notification provides the backbone for interfacing with various external systems like email servers and version control systems.
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公开(公告)号：US20180232737A1
公开(公告)日：2018-08-16
申请号：US15950859
申请日：2018-04-11
IPC分类号： G06Q20/40
CPC分类号： G06Q20/4016
摘要： A system and method for detecting a test event involving a financial transaction device at a merchant having a merchant profile is disclosed. The method includes receiving data associated with a transaction involving a financial transaction device; calculating a score using at least the transaction data; comparing the score to a threshold value; and attaching a merchant probe flag to the merchant profile if the score exceeds the threshold value. The merchant probe flag indicates a likelihood that a test event has occurred at the merchant based on the score. If a test event has occurred, then financial transaction devices involved in the test event can have their profiles updated to reflect that they have been probed. If a financial transaction device that has been probed is used in a subsequent transaction, then a specialized fraud scoring model can be used to provide an improved fraud risk score.
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