A credit risk monitoring method integrating a deep belief network and an isolated forest algorithm

Abstract

The invention discloses an isolated forest classification method used for credit risk monitoring and integrated with a deep belief network, and the method comprises the steps: employing the deep belief network to achieve the feature learning of high-dimensional data, and obtaining the optimal low-dimensional representation of an original data set; combined with linear time complexity, an unsupervised learning algorithm-Isolated forest algorithm for anomaly detection; And finally, achieving optimization of important parameters of the isolated Sensen algorithm through a particle swarm optimization algorithm and a simulated annealing algorithm, and forming a final DBN- iForst model for credit risk monitoring, And identifying the loan default.

Description

technical field [0001] The invention belongs to the field of computer software, and relates to a credit risk monitoring method integrating a deep belief network and an isolated forest algorithm (DBN-iForest). Background technique [0002] With the development of science and technology, the characteristics of financial market data are becoming more and more complex. Traditional machine learning methods are difficult to mine complex data features and cannot accurately reflect the characteristics of financial markets. Shallow neural networks have poor generalization ability for complex problems. However, the rule-based system usually cannot be updated in time, so the financial field needs to use advanced technology to build a new credit risk monitoring model. Deep learning is more suitable for the data characteristics of large data scale, high latitude and streaming data characteristics in the financial market, which has brought about an increase in its analysis requirements. T...

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Problems solved by technology

[0006] In the existing credit risk monitoring research literat

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