Training method and device for financial risk identification model, computer equipment and medium

Abstract

The invention discloses a training method and device for a financial risk identification model, computer equipment and a medium, and the method comprises the steps: obtaining first user data, with a first credit risk label, of a target domain financial project, inputting the first user data into a meta-learning device for training, and obtaining a classifier corresponding to the target domain financial project for risk identification. According to the method, the classifier corresponding to the target domain financial project is trained in a meta-learning mode, priori knowledge in a source domain task can be effectively migrated, so that the data volume of labeled samples required by model training is small, the generalization performance of the recognition model is improved, and the training process of the model is faster and more efficient. Besides, in the training process of the meta-learner, the source domain correlation among the categories of the task sets is learned, so that prior knowledge can be effectively migrated from tasks closer to the current target domain task during migration learning, and the accuracy of model recognition can be improved. The method can be widelyapplied to the technical field of machine learning.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a training method, device, computer equipment and media for a financial risk identification model. Background technique [0002] In recent years, artificial intelligence technology has developed rapidly, and classification applications based on machine learning have made great progress in many fields. Traditional machine learning is mainly single-task learning, that is, training a predictive model on a data set for a given task. This method has the defects of requiring more training data and poor model generalization ability. For classification tasks in some specific fields, the sample data often has the problem of insufficient labeling. For example, in the field of financial risk control, some new projects do not have a large number of user data containing credit records, which may lead to ineffective training of the model or training The accuracy of the model obtained...

AI Technical Summary

Problems solved by technology

Traditional machine learning is mainly single-task learning, that is, training a prediction model on a data set of a given task. This method has the defects of requiring more training data and poor model generalization ability.

For classification tasks in some specific fields, the sample data often has the problem of insufficient labeling. For example, in the field of financial risk control, some new projects do not have a large number of user data containing credit records, which may lead to ineffective training of the model or training The accuracy of the model obtained is not high, and the expected use effect cannot be achieved

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