Academic Journal of Computing & Information Science, 2021, 4(6); doi: 10.25236/AJCIS.2021.040604.
Yingjie Wei1, Jia Xing2, Pingye Zhou3, Tianye Tu4, Shangzhe Wei5
1Senior High School BCOS, Jiaxing, Zhejiang, China
2University of Sheffield, Sheffield, UK
3Huawei Foreign Language School, Shaoxing, Zhejiang, China
4Henan Experimental high school, Shang Qiu, Henan, China
5Kang Chiao International School, Suzhou, jiangsu, China
These authors contributed equally to this work
The issues we study are risk identification, credit granting, pre-loan and post-loan lending risks in consumer lending and trust loans. How to effectively evaluate and identify the potential default risk of borrowers and calculate the probability of default of borrowers before granting loans is a fundamental part of credit risk management in modern financial institutions. This paper focuses on the statistical analysis of historical loan data from banks and other financial institutions with the help of the idea of non-equilibrium data classification, and the use of the random forest algorithm to establish a loan default prediction model. Then a fixed threshold screening in the form of a black and white list is used. The limitation of the current method is that it cannot meet the lending needs of large-scale transaction classes and cannot be judged quickly. When the number of decision trees in a random forest is large, the space and time required for training are large. In addition, it is insensitive to data and has low accuracy. The experiments found that the machine learning-based financial lending risk prediction method can solve the problem better, and the experimental results phenotyped that the neural network and random forest algorithm outperformed the decision tree and logistic regression classification algorithm in prediction performance. In addition, by using the random forest algorithm to rank the importance of features, the features that have a greater impact on whether the final default can be obtained, so that the lending risk judgment in the financial sector can be more effective.
component; lending risk identification, fintech, machine learning, algorithmic models, risk prediction
Yingjie Wei, Jia Xing, Pingye Zhou, Tianye Tu, Shangzhe Wei. A Novel Financial Risk Identification Algorithm for Online Fintech Platform. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 6: 15-20. https://doi.org/10.25236/AJCIS.2021.040604.
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