Building a Credit Scoring Model Based on the Type of Target Variable

Abstract

With the rapid development of big data and internet technology, big data financial platform companies collect and organize massive data through their own platforms, improve credit scoring parameters, and use machine learning methods to conduct comprehensive and scientific credit scoring assessments. Thus, banks in the construction of credit scoring face big problems. Based on the limitations of the existing system and methods of personal credit score, it is necessary to study personal credit score based on machine learning methods, improve the parameters and scoring system of personal credit score, clarify data collection channels, and use dynamic desensitization technology to desensitize data, LOF test method to test outliers' data and a random forest method to complete missing data values. You then use the gradient boosting decision tree method to view the important indicators, process the tested indicators with a scorecard model based on logistic regression, and derive a personal credit score. Finally, the model is tested with a BP neural network and the model is used to predict the level of personal credit. The study shows that machine learning can further improve the accuracy of individuals' credit ratings and provide the scientific basis and reference for credit ratings of commercial banks.

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