Provided is a risk control method, relating to the technical field of intelligent decision making. The method comprises: collecting service data of a preset type and similar service data similar to the service data of the preset type, and inputting same into a pre-constructed neural network model; calculating a classification loss value of the similar service data, and an adaptive loss value of the service data of the preset type and the similar service data in each hidden layer of the neural network model; taking the sum of the classification loss value and the adaptive loss values of all the hidden layers as a total loss value, substituting same into the neural network model, and training the neural network model, so as to obtain an optimized neural network model; and inputting service data to be subjected to prediction into the optimized neural network model, outputting a label of the service data to be subjected to prediction, and obtaining, according to the label, a risk prediction value of the service data to be subjected to prediction. In the method, by calculating a multi-layer domain adaptive loss function on a neural network, noise in source domain data can be filtered out, thereby improving the performance of a prediction model.
RISK CONTROL METHOD, APPARATUS, ELECTRONIC DEVICE, AND COMPUTER-READABLE STORAGE MEDIUM