A study on personal credit risk assessment model based on integrated learning

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