Abstract
With the rapid development of the Internet, the traditional Internet financial risk prediction methods can no longer meet the needs of individuals and enterprises, so the concept of cloud computing arises at the historic moment. Cloud computing has subverted the traditional financial risk prediction method and has been widely studied and applied for its distributed, dynamic and autonomous characteristics. How to efficiently and reasonably schedule the resources of cloud data center and improve the accuracy of financial risk prediction is the focus of current research. How to quantify financial risk and financial risk early warning is one of the urgent problems to be solved. Under the framework of cloud computing, this paper combines the feature extraction and data weighting to study the user’s basic attribute data and a large number of downloaded APP types. After that, linear regression with penalty is used to construct the prediction model to improve insolvency. The accuracy of customer default judgment can realize local optimization, so as to improve the prediction and control of hidden risks of customer commercial bank loans and greatly reduce the default risk of bank loans.
Highlights
Cloud computing is developed on the basis of parallel computing, distributed computing, grid computing, and other technologies
In order to better illustrate, verify, and analyze the loan prediction method proposed to predict bank loan defaults, this paper will use open source real data set to verify the validity of the prediction method proposed in this paper [12]. is data set is customer desensitization data from a branch of a commercial bank in China [4]
Is means that the raw data cannot be applied directly to the bank credit risk analysis, must have the pretreatment process, and can be seen in table, which most of the attribute values are missing data, the biggest jump rate as high as 35%, but direct delete these values of missing items will have a significant impact on the overall integrity of records. erefore, we must use some appropriate data populating method for each missing attribute value
Summary
Cloud computing is developed on the basis of parallel computing, distributed computing, grid computing, and other technologies. It is a means of efficient resource utilization that can adapt to future large-scale computing needs. To sum up the dynamic force of its emergence, there are the two main reasons [6, 7]: (1) the rapid development of various technologies, virtualization technology, distributed computing is the basis of the development of cloud computing, in addition to automatic deployment and management, big data storage technology, powerful Map/Reduce mode and (2) in the field cost energy consumption equipment cost management and other aspects of the enterprise need to spend a lot of human resources and capital costs, this extremely dispersed highly closed machine room construction, thousands of enterprises need to face a major problem and stubborn disease [8].
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