TITLE:
Research on Personal Credit Evaluation Based on Mobile Telecommunications Data
AUTHORS:
Shaoyong Hong, Yan Zhang, Chun Yang
KEYWORDS:
Credit System, Weight of Evidence, Information Value, K-S Test, Logistic Regression
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.9 No.3,
August
2,
2021
ABSTRACT: With
the rapid development of big data technology, the personal credit evaluation
industry has entered a new stage. Among them, the evaluation of personal credit
based on mobile telecommunications data is one of the hotspots of current
research. However, due to the complexity and diversity of personal credit
evaluation variables, in order to reduce
the complexity of the model and improve the prediction accuracy of the model,
we need to reduce the dimension of the input variables. According to the data
provided by a mobile telecommunications operator, this paper divides the data
into a training sets and verification
sets. We perform correlation analysis on each indicator of the data in the training
set, and calculate the corresponding IV value based on the WOE value of the
selected index, then binning data with SPSS Modeler. The selected variables
were modeled using a logistic regression algorithm. In order to make the regression results more practical, we extract
the scoring rules according to the results of logistic regression, convert them
into the form of score cards, and finally
verify the validity of the model.