Machine-learning techniques involving monotonic recurrent neural networks

A method performed by one or more processing devices comprising:

• receiving a risk assessment query that identifies a target entity;
• providing a predictor variable for the target entity to a recurrent neural network (RNN);
• computing, with the RNN, an output risk indicator from the predictor variable, the RNN having one or more layers of nodes that are interconnected, the nodes comprising one or more hidden states having stored values based on predictor values in the predictor variable, the nodes having weights causing outputs of the RNN and input predictor variables input into the RNN to have a monotonic relationship, wherein the RNN is trained by performing training operations comprising:
• accessing training samples comprising training predictor variables and training risk indicators corresponding to the training predictor variables,
• computing, with the RNN, an updated value of an updated hidden state of the one or more hidden states as a first activation function applied to a first function, the first function comprising one or more weight matrices, and
• adjusting the weights of the nodes of the RNN to minimize a loss function subject to a set of monotonicity constraints, wherein the set of monotonicity constraints causes output risk indicators computed by the RNN and the input predictor variables input into the RNN to have the monotonic relationship, and wherein adjusting the weights comprising adjusting weight values in the one or more weight matrices;
• and
• transmitting the output risk indicator for the target entity to a second computing system for controlling, based on the output risk indicator, access by the target entity to one or more interactive computing environments.

A non-transitory computer-readable medium embodying program code for making a risk assessment, the program code comprising instructions that, when executed by a processor, cause the processor to perform operations comprising:

• providing a predictor variable for a target entity identified in a risk assessment query to a recurrent neural network (RNN);
• computing, with the RNN, an output risk indicator from the predictor variable, the RNN having one or more layers of nodes that are interconnected, the nodes comprising one or more hidden states having stored values based on predictor values in the predictor variable, the nodes having weights causing outputs of the RNN and input predictor variables input into the RNN to have a monotonic relationship, wherein the RNN is trained by performing training operations comprising:
• accessing training samples comprising training predictor variables and training risk indicators corresponding to the training predictor variables,
• computing, with the RNN, an updated value of an updated hidden state of the one or more hidden states as a first activation function applied to a first function, the first function comprising one or more weight matrices, and
• adjusting the weights of the nodes of the RNN to minimize a loss function subject to a set of monotonicity constraints, wherein the set of monotonicity constraints causes output risk indicators computed by the RNN and the input predictor variables input into the RNN to have the monotonic relationship, and wherein adjusting the weights comprising adjusting weight values in the one or more weight matrices;
• and
• causing the output risk indicator to be transmitted to a second computing system for controlling, based on the output risk indicator, access by the target entity to one or more interactive computing environments.