Prepayment and Default of Consumer Loans in Online Lending

2016 ◽  
Author(s):  
Zhiyong Li ◽  
Xiao Yao ◽  
Qing Wen ◽  
Wei Yang
Keyword(s):  
Consumer Loans ◽  
Prepayment And Default ◽  
Online Lending

Predicting Prepayment and Default Risks of Unsecured Consumer Loans in Online Lending

2018 ◽  
Vol 55 (1) ◽  
pp. 118-132 ◽  
Author(s):  
Zhiyong Li ◽  
Ke Li ◽  
Xiao Yao ◽  
Qing Wen
Keyword(s):  
Consumer Loans ◽  
Prepayment And Default ◽  
Default Risks ◽  
Online Lending

Effects of Regulation on Prepayment and Default of Subprime Mortgages

2008 ◽  
Author(s):  
Jevgenijs Steinbuks
Keyword(s):  
Subprime Mortgages ◽  
Prepayment And Default

Modeling Credit Risk of Portfolio of Consumer Loans

2007 ◽  
Author(s):  
Madhur Malik ◽  
Lyn C. Thomas
Keyword(s):  
Credit Risk ◽  
Consumer Loans

A semi-parametric ensemble model for profit evaluation and investment decisions in online consumer loans with prepayments

2021 ◽  
Vol 107 ◽  
pp. 107485
Author(s):  
Ke Li ◽  
Fanyin Zhou ◽  
Zhiyong Li ◽  
Wanqing Li ◽  
Feng Shen
Keyword(s):  
Investment Decisions ◽  
Ensemble Model ◽  
For Profit ◽  
Consumer Loans ◽  
Online Consumer

scholarly journals Understanding the Recent Growth in Consumer Loans and Credit Cards in Emerging Markets: Evidence from Turkey

2017 ◽  
Vol 53 (10) ◽  
pp. 2333-2346 ◽  
Author(s):  
Murat Mazibaş ◽  
Yusuf Tuna
Keyword(s):  
Emerging Markets ◽  
Credit Cards ◽  
Consumer Loans ◽  
Recent Growth

Assessing Bank Competition for Consumer Loans

2015 ◽  
Author(s):  
Wilko Bolt ◽  
David B. Humphrey
Keyword(s):  
Bank Competition ◽  
Consumer Loans

scholarly journals Performance Analysis of Peer-to-Peer Online Lending Platforms in China

2018 ◽  
Vol 10 (9) ◽  
pp. 2987 ◽  
Author(s):  
Pingfan Song ◽  
Yunzhi Chen ◽  
Zhixiang Zhou ◽  
Huaqing Wu
Keyword(s):  
Risk Management ◽  
Information Disclosure ◽  
Peer To Peer ◽  
The State ◽  
Enterprise Group ◽  
Measure Data ◽  
Unexpected Result ◽  
Data Envelopment ◽  
State Owned Enterprise ◽  
Online Lending

In this paper we intend to check the performance of Peer-to-Peer online lending platforms in China. Different from commercial banks, Peer-to-Peer (P2P) platforms’ business process is divided into the market-expanding stage and the risk-managing stage. In the market-expanding stage, platforms are intended to help borrowers attain more money, and in the risk-managing stage, platforms try their best to ensure that the lenders’ money is repaid on time. Thus, with a sample of 66 leading big P2P platforms, and a novel two-stage slacks-based measure data envelopment analysis with non-cooperative game, the performance efficiency of each stage as well as the comprehensive efficiency are evaluated. The results show that the leading big platforms are good at managing the risk, although risk management is not the major concern of most P2P platforms in China. We also find that average performance efficiency of the platforms that are located in non-first tier cities is higher than that in first tier cities. This unexpected result indicates that development of the P2P industry may relieve the severe distortion of resource allocation and efficiency loss arising from unbalanced regional development. Then dividing the platforms into different groups according to different types of ownership, we verify that performance efficiency of the P2P platforms from the state-owned enterprise group is in a dominant position, and the robustness check indicates that the major advantage of the state-owned enterprise (SOE) group mainly lies in the risk management. We also make a further study to figure out the sources of inefficiency, finding that it mainly arises from the shortage of lenders, the lack of average borrowing balance, and the insufficient transparency of information disclosure. In the last section we conclude our research and propose some advice.

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