1. A system comprising: a processing device; and a memory device in which instructions executable by the processing device are stored for causing the processing device to: receive a plurality of predictor variables, wherein each predictor variable corresponds to an entity; determine a correlation between each predictor variable and an outcome; generate a neural network that includes a hidden layer for determining a relationship between each predictor variable and a risk indicator based on the correlation, wherein the risk indicator is a level of risk associated with the entity and wherein the neural network is operable for determining whether a monotonic relationship exists between each predictor variable and the risk indicator; and iteratively adjust the neural network so that the monotonic relationship exists between each predictor variable and the risk indicator as determined by the neural network, wherein the processing device is configured to: adjust the neural network by adjusting at least one of a number of nodes in the hidden layer of the neural network, a predictor variable in the plurality of predictor variables, or a number of layers in the neural network; determine, using the neural network, the risk indicator based at least partially on the predictor variables after the monotonic relationship exists between each predictor variable and the risk indicator; determine, based on a rate of change of the risk indicator with respect to the predictor variable, that the monotonic relationship exists between each predictor variable and the risk indicator; determine, using the neural network, an impact of each predictor variable on the risk indicator; and generate, using the neural network, an adverse action code associated with each predictor variable that indicates the impact of each predictor variable on the risk indicator.