Personal credit risk evaluation method and evaluation system for large-scale unbalanced credit data

Abstract

The invention discloses a personal credit risk evaluation method and evaluation system for large-scale unbalanced credit data. The method comprises steps that firstly, according to the acquired high-dimensional large-scale unbalanced historical credit data, a training matrix X constructed by the acquired historical data set is constructed, and the adaptive elastic network characteristic selectionalgorithm is utilized to implement dimensional reduction for the given historical credit data set; and secondly, a training sample set after dimension reduction is divided into a few of sample classesand a plenty of sample classes, the membership degree of each sample is calculated according to the cluster-like centroid distance exponential attenuation function, a weight matrix W is constructed,relevant parameters are set, and an IWELM model is utilized to implement personal credit risk evaluation. The method is advantaged in that a high-degree unbalanced problem shown by the large-scale credit data is overcome, the speed and efficiency of personal credit risk evaluation are further improved, and reliability and credibility of the evaluation result are improved.

Description

technical field [0001] The invention relates to the field of data evaluation, in particular to a personal credit risk evaluation method and evaluation system for large-scale unbalanced credit investigation data. Background technique [0002] At present, personal credit risk assessment methods for multi-channel, fragmented, heterogeneous, semi-structured and unstructured credit data are increasingly valued by financial service institutions. Due to the large-scale, high-dimensional, sparse and highly class-imbalanced characteristics of the currently collected credit risk assessment data, it is required to reduce the corresponding attributes of the data before training the assessment model to improve the quality of the original data set and information density, so as to help establish a more effective personal credit risk assessment model. In this process, it is necessary to use the corresponding feature selection algorithm to reduce the dimensionality of the rough original dat...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a personal credit risk assessment method and system for large-scale unbalanced credit data for large-scale, high-dimensional, sparse and highly class-imbalanced problems presented by current credit data, specifically The technical solution is as follows:

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