Personal credit assessment method and system based on fusion neural network feature mining

Abstract

The invention relates to a credit assessment technology, and aims to provide a personal credit assessment method and system based on fusion neural network feature mining. The method comprises the steps that behavior data of an individual user are preprocessed and checked and then subjected to matrix processing, and the obtained data serve as input of an LSTM model and a CNN model at the same time; in the LSTM model, sequentially processing by an embedding layer, a bidirectional long short-term memory neural network and an attention mechanism layer, and outputting a time sequence behavior feature vector extracted from the data; in the convolutional neural network model, processing is carried out through a convolutional layer and a pooling layer in sequence, and local behavior feature vectors extracted from the data are output; and carrying out vector splicing on the two types of feature vectors, taking the spliced feature vectors as input of an XGBoost classifier, and carrying out training to finally obtain a personal credit evaluation result. Compared with the prior art, the method has the characteristics of comprehensive index coverage, wide processing index source, advanced modeling mode, flexible model expansion, complete and effective feature extraction and accurate result.

Description

technical field [0001] The invention relates to a credit evaluation technology based on big data, in particular to a personal credit evaluation method and system based on fusion neural network feature mining. Background technique [0002] At present, with the gradual rise of financial technology and the maturity and stability of Internet finance, the importance of personal credit information business in the entire financial field has become increasingly prominent. However, the current domestic credit reporting industry is still in its infancy, with the problems of a small number of credit reporting agencies and a generally small scale. Traditional personal credit risk assessment methods have been unable to adapt to changes in the environment and cannot meet the development needs of the financial industry. Therefore, in this context, it is urgent to build an intelligent personal credit evaluation system. [0003] With the rapid development of Internet finance, credit invest...

AI Technical Summary

Problems solved by technology

The above are credit evaluation methods in specific application scenarios, but since credit evaluation is a binary classification problem about credit quality, and different classification algorithms have different usage scenarios, they cannot show ideal results on different data sets. Therefore, the classification methods proposed in the above inventions are not universal and cannot be applied to general situations; secondly, the above inventions use traditional artificial feature extraction methods for modeling, relying on manual basic feature extraction and feature The combination will cause problems such as low derivation efficiency, high calculation cost, and incomplete features.

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