Microsoft Technology Licensing, LLC

Yuting Jia

Jayaram N M Nanduri

Anand Ravindra Oka

Cezary Antoni Marcjan

Shoou-Jiun Wang

John Alan Beaver

Jayaram Nm Nanduri

Adam Feldman Reinhardt

Yung-Wen Liu

Kiyoung Yang

Murali Krishna Manjunath

Yini Zhang

Hang Xu

Liang-Yu Chen

Yiqing Wang

Jayaram Naga Mrutyum Nanduri

Anton Khokhlov

Huiying Mao

Adam Reinhardt

Different automatic tasks are facilitated via outlier detection in datasets using a Weighted Histogram-based Outlier Scoring (W-HBOS). An initial set of features is extracted from a processed dataset. The initial set of features is further filtered by applying robust statistics for size reduction. A second round of automatic feature selection is implemented based on maximum-entropy estimation so that a selected set of features that can give maximum possible information from different dimensions towards detecting anomalies are selected. The selected set of features are transformed to generate principal components that are provided to the W-HBOS-based model for outlier detection. A subset of outliers in one of the directions can be selected and reason codes are identified using back transformation for the execution of a desired automatic task.

Machine learning outlier detection using weighted histogram-based outlier scoring (W-HBOS)

Devices and methods for securing electronic transactions in a computing network are configured to receive a transaction, access transaction contextual data for the transaction, and apply one or more authentication decision rules to the transaction contextual data to determine initial authentication assessment information for the transaction. Further, aspects are configured to convert the initial authentication assessment information into a condensed authentication assessment indicator having an assessment value selected from a plurality of assessment values based on an assessment value definition, and transmit the condensed authentication assessment indicator. Also, aspects receive a transaction authentication decision based in part on the condensed authentication assessment indicator, and process or deny processing of the transaction based on the transaction authentication decision.

Systems and methods for authenticating an electronic transaction

Risk scores are generated by systems that use features or inputs from the current transactions, some summary statistics like velocity and some statistics calculated in batch mode which traverse hierarchical classification levels of entities, including classified attribute levels of known transactions, to identify neighborhoods of related entities and related transactions. Corresponding records of transaction information are extracted from or are otherwise generated from neighborhoods of the related transactions associated with the defined neighborhoods of entities/transactions. These records of transaction information are then used by a consortium risk engine to generate composite risk scores associated with information from multiple merchants/entities and that were only identified by traversing the hierarchical classification of the entities and/or the corresponding transactions, which are at least tangentially related to the new transaction(s), and which may be the basis for approving or denying the new transaction(s) and/or sending the new transaction(s) for manual review.

Knowledge neighbourhoods for evaluating business events

A device in a data processing system for training machine learning models receives a transaction and forwards it to at least one of a plurality of integrated control action models that use outputs of one model as inputs to other models. The models are machine learning models jointly trained for taking each control action of a plurality of control actions on the transaction to maximize an objective function based on probabilities of the control actions matching corresponding target control actions. The machine learning models include a risk model that outputs risk prediction information for a first control action that indicates whether or not to initiate processing of the transaction. The device further receives the risk prediction information from the risk model, and executes at least the first control action based on the risk prediction information.

Machine learning system for taking control actions

Embodiments disclosed herein are related to computing systems and methods for determining a risk score for a plurality of data transactions. In the embodiments, a first risk score module may receive data transactions. The first risk score module may then determine a first risk score for each of the data transactions. A second risk score module that is different from the first risk score module may receive each of the first risk scores determined by the first risk score module as an input. The second risk score module may determine a second risk score based in part on the input first risk scores for each of the data transactions. The second risk scores may specify if each of the data transactions is to be approved or rejected by the computing system.

Long-term short-term cascade modeling for fraud detection

Embodiments disclosed herein are related determining a risk score for one or more data transactions. Current data transactions that are associated with one or more current attributes are received. Stored data transactions associated with stored attributes are accessed. A plurality of the stored attributes are selected. A first sliding window and a second sliding window are selected. A duration of the second sliding window is longer than a duration of the first sliding window and encompasses the duration of first sliding window. Risk information for those stored data transactions that are associated with the plurality of attributes is determined. The risk information is determined during the duration of both the first and second sliding windows and is indicative of a level fraud that is occurring. The determined risk information and the current attributes are used to generate a risk score for the current data transactions. The current data transactions are approved or rejected based on the risk score.

Hierarchical profiling inputs and self-adaptive fraud detection system

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Redmond, WA, United States of America

Yuting Jia