Abstract
This paper proposes a multi-indicator search study based on the social network perspective for borrowing and lending customers. When studying P2P lending and losing customers, according to the division of interpersonal relationship in sociology and the extent to which the client can be debited in reality, it is divided into three kinds of relationship networks: blood relationship network, geo-relationship network and business relationship network, assisting to combine the two-mode subordinate network of the lost customers, and focusing on analyzing its scale, density and other indicators, and hope to find the target lost customers. Key figures in the social network of interpersonal relationships, message communicators, behavioral influencers, etc., to achieve the search for lending customers after their sudden loss. For the first time, this paper applies the social network theory to the search for borrowed and lost customers and proposes the MIL algorithm. It proposes a new idea for the hiddenness of the loan client’s loss of connection, and accelerates the governance of the ill-fated phenomenon of loan and loan loss effectiveness. This paper also analyzes the case and proposes relevant suggestions in connection with a loss event in Guangzhou.
Highlights
In order to facilitate the lending process, P2P lending has insufficient information on most borrowing customers, especially if there is insufficient information such as collateral and financial information, so that once the lending customers lose contact, we will have nowhere to look for, given China Society has always been a society connected by interpersonal relationships, so it is of great significance for P2P lending customers to use social network search tools
The assignment to the individual social network is combined with the MIL method algorithm to first calculate the multipath as follows, in this case, It is determined by the interpersonal relationship network that the node 1 arrives at the multipath of the node 5, and the search is interrupted when the shortest path occurs when the node is not searchable or the node does not cooperate
This paper studies the application research of interpersonal social network to the search of borrowed and lost customers
Summary
With the growing difficulties of SMEs’ main loans, private capital has become more and more large, and some open cities have allowed “private. Lin M (2009) [3] uses a large sample of the complete and failed list of Prosper.com, the largest online P2P lending market, to test whether social networks lead to better loan outcomes and find that online social networks can alleviate information on both borrowers and lenders. Freedman S (2017) [5] studied whether leading P2P lending sites use social network data to promote online markets. The results show that the relationship between online social networks helps to alleviate information asymmetry in the loan process. BC Collier (2010) [18] By embedding personal reputation in the community’s reputation, incentives allow peers to choose high-quality borrowers and generate more expensive information signals, thereby reducing the typical adverse selection in any agent relationship and Moral hazard risk. Exploring a summary and suggestions on how to use the social network method for those who have lost their associations under the premise that China’s current credit system is not perfect
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