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Royal Bank of Canada

Jingyi Gao

Kanika Vij

Diane Elizabeth Fenton

Vincent Chiu-Hua Huang

Gladys Leung

Adam Lazure

Esther Choi

Igor Reshynsky

Jinda Yang

Hameet Jassal

Rowan Comish

William Kurelek

Emma Hu

Min Liang

Disclosed are systems, methods, and devices for data anomaly detection. A signal reflective of an input data set having a plurality of dimensions is received. Co-variance across said plurality of dimensions is assessed. Upon said assessing, at least a portion of the input data set is transformed into a dimensionality-reduced data set. For each given data point in the dimensionality-reduced data set, an anomaly score informative of whether said given data point is an anomaly is calculated.

System and method for multivariate anomaly detection

Systems and methods of monitoring for anomalous data records. The system conducts a method including: receiving a data record associated with at least one meta attribute to determine whether subsequent processing of the data record is warranted; generating an anomaly prediction for the data record based on a detection model and the at least one meta attribute associated with the data record, the detection model defined by a plurality of score distribution representations based on quantile bins and a dynamic quantile weight for providing an interim anomaly measure corresponding to respective score distribution representations, wherein the anomaly prediction is generated based on a combination of interim anomaly measures associated with respective meta attributes associated with the data record; and transmitting a signal representing the anomaly prediction for presentation at a user device for identifying one or more data records for subsequent data processes.

Systems and methods for dynamically managing data sets

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Toronto, Canada

Jingyi Gao