Author(s)

Yuchun Li ¹, Shaojie Yang ¹

Affiliation(s)

¹ Accounting Institute, Haojing College of Shaanxi University of Science &Technology, Xianyang, Shaanxi, 712046, China

Corresponding Author

Yuchun Li

ABSTRACT

Traditional risk models do not have a unified standard and there are loopholes in the connection. Based on machine learning and big data technology, this paper analyzes the model of applying machine learning to risk assessment and the resulting improvement in the accuracy of risk assessment. How to analyze the IoT-enabled financial management of credit in depth and accurately, develop an effective credit risk assessment model, make up for the weaknesses of the existing credit research industry is important. The evaluation report given by many machine learning algorithms has a high probability, and this high probability evaluation result is correct for making a risk assessment decision. The elimination of outliers and the selection of feature variables, the prediction accuracy rate reached 85%. At the level of left and right, the credit risk of online lending is very complicated in reality, and this accuracy has good application prospects.

KEYWORDS

Machine Learning, Big Data, Risk Assessment, Internet of Things

CITE THIS PAPER

Yuchun Li, Shaojie Yang, Machine Learning and Big Data Application for Risk Assessment of IoT-enabled Financial Management of Credit. Financial Engineering and Risk Management (2024) Vol. 7: 170-179. DOI: http://dx.doi.org/10.23977/ferm.2024.070522.

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