A study on personal credit risk assessment model based on integrated learning
DOI:
https://doi.org/10.54691/bcpbm.v32i.2994Keywords:
Credit Risk, Data Mining, Decision Tree, Integrated Learning, Logistic Regression.Abstract
Financial risk control is an important means to control capital security. Under the background of market economy and inclusive finance, the demand for personal credit loans is increasing rapidly. Therefore, it is very important to control financial risks. However, in the financial field, due to the requirements of security, data is scarce and large-scale data cannot be obtained, so many researchers cannot carry out detailed data analysis and technical research, and can only carry out simple theoretical analysis. However, the current financial risk control mostly relies on manual operation, which is inefficient. In order to solve this problem, this paper firstly obtains a large number of personal credit loan information from foreign websites. Then, the feature engineering technology is used to clean the data. Finally, the data mining technology in the field of artificial intelligence is used for improvement, which greatly promotes the automation and intelligent process of financial risk control. In this paper, the data mining algorithm is used to classify the credit loan dataset. The results show that our model achieves good results and can effectively avoid nearly 70% default risk, and the time cost is very low.
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