The New Membership of Loan Club—P2P Online Lending

2016 ◽  
pp. 55-72 ◽  
Author(s):  
Jiazhuo G. Wang ◽  
Juan Yang
Keyword(s):  
Online Lending

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.

scholarly journals Adverse Selection in P2P Lending: Does Peer Screening Work Efficiently?—Empirical Evidence from a P2P Platform

2021 ◽  
Vol 9 (4) ◽  
pp. 73
Author(s):  
Yao Wang ◽  
Zdenek Drabek
Keyword(s):  
Adverse Selection ◽  
Rapid Development ◽  
Financial System ◽  
Decision Making Process ◽  
Type Ii Error ◽  
Close Attention ◽  
Large Dataset ◽  
The Past ◽  
P2p Lending ◽  
Online Lending

The rapid development of online lending in the past decade, while providing convenience and efficiency, also generates large hidden credit risk for the financial system. Will removing financial intermediaries really provide more efficiency to the lending market? This paper used a large dataset with 251,887 loan listings from a pioneer P2P lending platform to investigate the efficiency of the credit-screening mechanism on the P2P lending platform. Our results showed the existence of a TYPE II error in the investors’ decision-making process, which indicated that the investors were predisposed to making inaccurate diagnoses of signals, and gravitated to borrowers with low creditworthiness while inadvertently screening out their counterparts with high creditworthiness. Due to the growing size of the fintech industry, this may pose a systematic risk to the financial system, necessitating regulators’ close attention. Since, investors can better diagnose soft signals, an effective and transparent enlargement of socially related soft information together with a comprehensive and independent credit bureau could mitigate adverse selection in a disintermediation environment.

Why segmentation matters: a Machine Learning approach for predicting loan defaults in the Peer-to-Peer (P2P) Financial Ecosystem

2021 ◽  
Vol 16 (2) ◽  
pp. 35-49
Author(s):  
Adamaria Perrotta ◽  
◽  
Georgios Bliatsios ◽  
Keyword(s):  
Machine Learning ◽  
Area Under The Curve ◽  
Peer To Peer ◽  
Weight Of Evidence ◽  
K Nearest Neighbors ◽  
Default Prediction ◽  
Sequential Feature Selection ◽  
Loan Defaults ◽  
The One ◽  
Online Lending

Peer-to-Peer (P2P) lending is an online lending process allowing individuals to obtain or concede loans without the interference of traditional financial intermediaries. It has grown quickly the last years, with some platforms reaching billions of dollars of loans in principal in a short amount of time. Since each loan is associated with the probability of loss due to a borrower's failure, this paper addresses the borrower's default prediction problem in the P2P financial ecosystem. The main assumption, which makes this study different from the available literature, is that borrowers sharing the same homeownership status display similar risk profile, thus a model per segment should be developed. We estimate the Probability of Default (PD) of a borrower by using Logistic Regression (LR) coupled with Weight of Evidence encoding. The features set is identified via the Sequential Feature Selection (SFS). We compare the forward against the backward SFS, in terms of the Area Under the Curve (AUC), and we choose the one that maximizes this statistic. Finally, we compare the results of the chosen LR approach against two other popular Machine Learning (ML) techniques: the k Nearest Neighbors (k-NN) and the Random Forest (RF).

Risks in the Online Lending Industry

2015 ◽  
pp. 119-138 ◽  
Author(s):  
Jiazhuo G. Wang ◽  
Hongwei Xu ◽  
Jun Ma
Keyword(s):  
Online Lending

Introduction to Online Lending

2015 ◽  
pp. 1-16
Author(s):  
Jiazhuo G. Wang ◽  
Hongwei Xu ◽  
Jun Ma
Keyword(s):  
Online Lending

scholarly journals Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Aiwen Niu ◽  
Bingqing Cai ◽  
Shousong Cai
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Credit Risk ◽  
Big Data Analytics ◽  
Assessment Model ◽  
Effective Control ◽  
Credit Score ◽  
Credit Risk Assessment ◽  
Index Weight ◽  
Online Lending

With the continuous development of big data technology, the data of online lending platform witness explosive development. How to give full play to the advantages of data, establish a credit risk assessment model, and realize the effective control of platform credit risk have become the focus of online lending platform. In view of the fact that the network loan data are mainly unbalanced data, the smote algorithm is helpful to optimize the model and improve the evaluation performance of the model. Relevant research shows that stochastic forest model has higher applicability in credit risk assessment, and cart, ANN, C4.5, and other algorithms are also widely used. In the influencing factors of credit evaluation, the weight of the applicant’s enterprise scale, working years, historical records, credit score, and other indicators is relatively high, while the index weight of marriage and housing/car production (loan) is relatively low.

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