Equifax Inc.

Xiao Huang

Jonathan Boardman

Yan Wang

Matthew Turner

Jeffery Dugger

Md Shafiul Alam

Various aspects involve a monotonic recurrent neural network (MRNN) trained for risk assessment or other purposes. For instance, the MRNN is trained to compute a risk indicator from a predictor variable. Training the MRNN includes adjusting weights of nodes of the MRNN subject to a set of monotonicity constraints, wherein the set of monotonicity constraints causes output risk indicators computed by the RNN to be a monotonic function of input predictor variables. The trained monotonic RNN can be used to generate an output risk indicator for a target entity.

Machine-learning techniques involving monotonic recurrent neural networks

Various aspects involve a lagged prediction model trained for risk assessment or other purposes. For instance, a risk assessment computing system receives a risk assessment query for a target entity and provides an input predictor record for the target entity to a lagged prediction model. The input predictor record includes a first group of lagged values from a first time-series attribute associated with the target entity. The lagged prediction model is trained by implementing a group feature selection technique configured to select the first time-series attribute as input and to deselect a second time-series attribute associated with the target entity. The risk assessment computing system computes an output risk indicator from the input predictor record and transmits the output risk indicator to a remote computing system. The output risk indicator can be used to control access by the target entity to one or more interactive computing environments.

Techniques for prediction models using time series data

Systems and methods for automated historical risk assessment for risk mitigation in online access control are provided. An entity assessment server can receive a request to assess a risk indicator change from a first risk indicator to a second risk indicator. For each attribute used to generate the first risk indicator and second risk indicator, a first impact can be determined for changing from the first risk indicator to a third risk indicator between the first risk indicator and the second risk indicator. A second impact similarly can be determined for changing from the third risk indicator to the second risk indicator. Aggregating the first impact and the second impact can determine a total impact of each attribute. Assessment results can be generated to include a list of attributes ordered according to the respective total impact and transmitted to a remote computing device for use in improving the risk indicator.

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Marietta, GA, United States of America

Xiao Huang