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Multiangle P2P Borrower Characterization Analytics by Attributes Partition Considering Business Process
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DOI：10.1109/MIS.2020.2986973.png)
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Abstract
En 中文
In the research of P2P lending data, the study of borrower characteristics is of great value for the establishment of target customers and risk management. Because of high dimensionality, mixed attributes, different importance, and different generation time of information, P2P lending data often leads to the mining results unable to reflect the important borrower characteristics that affect the approval results and the approval loan amount. In this article, we are the first to propose the attributes partition of lending data considering the business process to classify variables into different types. Furthermore, we propose a multiangle data mining method for lending data by attributes partition considering the business process to discover the characteristics of P2P borrowers from multiple perspectives. Experimental results on the real dataset demonstrate that the method depicts the important characteristics of borrowers that affect the approval results and the loan amount, makes the research on P2P borrower characteristics more comprehensive and specific, and provides new ideas for the research on high-dimensional lending data.
Keywords:
Data mining
Risk management
Global Positioning System
Intelligent systems
Internet
Loans and mortgages
Internet
Mining methods and algorithms
Feature evaluation and selection
Data mining
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