The development of science and technology promotes the constant changes of consumer finance, but also brings some financial credit risks. In particular, with the continuous development of Internet finance, financial credit risk is increasingly difficult to control. Based on machine learning algorithm, this study improved the machine learning algorithm and named it MLIA algorithm. Meanwhile, this study decomposes the objective function into weighted sums of several basis functions. This study uses three typical test functions to compare the performance of MLIA prediction algorithm and logistic prediction algorithm. Simultaneously, this study analyzes the performance of MLIA financial credit risk prediction model by taking the data of an Internet financial company as an example. In addition, this study used the AUC (area under curve) value as a specific indicator of model performance verification. Research shows that machine learning has a good predictive effect on MLIA financial credit risk prediction and can provide theoretical reference for subsequent related research.
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This work is supported by Project of the China Postdoctoral Science Foundation (No. 2018M643213).
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Ma, X., Lv, S. Financial credit risk prediction in internet finance driven by machine learning. Neural Comput & Applic 31, 8359–8367 (2019). https://doi.org/10.1007/s00521-018-3963-6
- Machine learning
- Risk prediction
- Logistic model