Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.