Financial product recommendation method based on GAMxNN model

Abstract

The invention discloses a financial product recommendation method based on a GAMxNN model, relates to the technical field of bank financial product recommendation systems, and solves the problem thatdata prediction in an existing bank financial product recommendation system is either low in precision and high in interpretability or high in precision and low in interpretability. The method comprises: 1, performing data cleaning and preprocessing; 2, feature selection: determining the importance proportion of each variable by adopting a random forest algorithm, and reserving the feature variable with the highest importance proportion for the feature variables with the correlation coefficient of 0.9 or above; 3, training a GAMxNN model, selecting AUC as a model evaluation index, and obtaining an optimal hyper-parameter of the model; and 4, inputting a to-be-recommended customer characteristic value, and obtaining a target variable prediction result. According to the method, high-precision and high-interpretability bank financing product recommendation prediction is achieved, recommendation and non-recommendation results can be given, and meanwhile main influence factors of recommendation and non-recommendation can be given.

Description

technical field [0001] The invention relates to the technical field of bank wealth management product recommendation systems, in particular to a method for recommending wealth management products based on a GAMxNN model. Background technique [0002] In recent years, the development of neural networks has brought significant breakthroughs in the field of machine learning and artificial intelligence. Complex network structures emerge in endlessly, and have achieved great success in the fields of computer vision and natural language processing. In addition to the predictive performance of the model, transparency and interpretability are also important criteria for assessing whether a machine learning model is trustworthy. [0003] At present, in terms of financial product recommendation, if a model with a relatively simple structure is used, such as: linear model, logistic regression, decision tree, etc., its explanatory power is better, but the prediction accuracy is relativ...

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Problems solved by technology

[0004] In summary, the purpose of the present invention is to solve the problem of either low precision and high interpretability or high precision and low interpretability in

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