Marketing Strategies for P2P Lending Project Success: Focusing on Setting the Maximum Interest Rate Based on Creditworthiness

2021 ◽  
Vol 36 (2) ◽  
pp. 107-125
Author(s):  
Ji Hyeon Hwang ◽  
Kang Jun Choi ◽  
Jae Young Lee
Keyword(s):  
Interest Rate ◽  
Project Success ◽  
Marketing Strategies ◽  
P2p Lending

What Influences the Market Outcome of Online P2P Lending Marketplace?

2015 ◽  
Vol 23 (3) ◽  
pp. 23-40 ◽  
Author(s):  
Yun Xu ◽  
Chuan Luo ◽  
Dongyu Chen ◽  
Haichao Zheng
Keyword(s):  
Social Capital ◽  
Interest Rate ◽  
Online Communities ◽  
Global Perspective ◽  
Archival Data ◽  
Bank Institution ◽  
Creditor Bank ◽  
Prior Literature ◽  
P2p Lending ◽  
The U.S

Online Peer-to-Peer (P2P) lending marketplaces allow individuals to lend and borrow directly among each other without the mediation of a creditor bank institution. Prior literature has examined online P2P, but has largely been limited to the Western context. This paper thus explores how social capital and other factors influences online P2P lending in the U.S. and China. Based on the archival data of Prosper and PPDai, we compare market outcome of two online P2P lending marketplaces in the U.S. and China. The empirical results show that social capital is not equally important in different online communities. Social capital seems to be more influential for likelihood of getting funded in China than in the U.S. In contrast, social capital has influence on interest rate in the U.S. only. The authors' study thus extends current understanding about how social capital influences online communities to a global perspective.

scholarly journals Optimizing the Borrowing Limit and Interest Rate in P2P System: From Borrowers’ Perspective

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Zhihong Li ◽  
Lanteng Wu ◽  
Hongting Tang
Keyword(s):  
Neural Network ◽  
Interest Rate ◽  
Bp Neural Network ◽  
Interval Estimation ◽  
Real World Data ◽  
Proposed Model ◽  
P2p Lending ◽  
New Perspective ◽  
P2p System ◽  
Benefits And Costs

P2P (peer-to-peer) lending is an emerging online service that allows individuals to borrow money from unrelated person without the intervention of traditional financial intermediaries. In these platforms, borrowing limit and interest rate are two of the most notable elements for borrowers, which directly influence their borrowing benefits and costs, respectively. To that end, this paper introduces a BP neural network interval estimation (BPIE) algorithm to predict the borrowers’ borrowing limit and interest rate based on their characteristics and simultaneously develops a new parameter optimization algorithm (GBPO) based on the genetic algorithm and our BP neural network predictive model to optimize them. Using real-world data from http://ppdai.com, the experimental results show that our proposed model achieves a good performance. This research provides a new perspective from borrowers in exploring the P2P lending. The case base and proposed knowledge are the two contributions for FinTech research.

An Evolutionary Behavior Forecasting Model for Online Lenders and Borrowers in Peer-to-Peer Lending

2017 ◽  
Vol 34 (01) ◽  
pp. 1740008 ◽  
Author(s):  
Wei Liu ◽  
Li-Qiu Xia
Keyword(s):  
Interest Rate ◽  
Evolutionary Game ◽  
Mutual Effect ◽  
Peer To Peer ◽  
Forecasting Model ◽  
Private Lending ◽  
P2p Lending ◽  
Numeric Experiment ◽  
The Interest Rate ◽  
Borrowing Cost

Online peer-to-peer (P2P) lending is an emerging financial mode that combines the Internet with private lending to provide unsecured lending among individuals. The interest rate and risk depend on online lenders and borrowers’ behavior choices and game in the context of P2P lending. In this paper, we propose an evolutionary behavior forecasting model for online participants based on the risk preference behavior of lenders and the credit choice of borrowers. We highlight four evolutionary equilibrium states of online lenders and borrowers’ behavior and their effects on the risk of online P2P lending platforms. We run a numeric experiment using the Paipaidai platform in China as a case and find that the evolutionary behavior of online lenders and borrowers is determined by the mutual effect of the interest rate, information gathering cost, borrowing cost, and yield rate. This paper uses evolutionary game methodology to analyze online P2P lending behavior in China and explores P2P fund success from the dual perspective of lenders and borrowers.

scholarly journals Can Readability of Loan Description Affect Loan Success Rate and Loan Cost?: A Textual Analysis of P2P Loan Description

2020 ◽  
Vol 31 (3) ◽  
pp. 302-313
Author(s):  
Haitao Si ◽  
Siqi Jiang ◽  
Yizhuo Fang ◽  
Usman Muhammad
Keyword(s):  
Interest Rate ◽  
Success Rate ◽  
Information Transfer ◽  
Textual Analysis ◽  
Educational Background ◽  
Loan Market ◽  
Descriptive Information ◽  
Incremental Information ◽  
P2p Lending ◽  
Important Form

Text is an important form of information transfer. In a P2P (Peer-to-Peer) loan market with severe information asymmetry, loan descriptive information voluntarily written by a borrower may generate significant influence on investors’ decision-making. As an important language feature, readability determines whether the audience can accurately identify textual contents. To discuss the influence of readability of loan description on borrowing behaviors, the data of a Chinese P2P lending platform Renrendai during 2013–2017 were used in this study. Readability index of loan description was constructed through the textual analysis method, followed by an empirical verification of the influences of readability of loan description on loan success rate and loan cost. Results indicate that the readability of loan description in China’s P2P loan market can generate incremental information, and the readability of loan description has evident nonlinear relations with loan success rate and loan interest rate. Moreover, the readability of loan description presents a “reverse U-shaped” relation with loan success rate and a “U-shaped” relation with loan interest rate. Demographic information (gender and educational background) of borrowers will not influence investors’ feedback behaviors toward the readability of loan description. Conclusions provide empirical evidence for standardizing loan descriptive information on P2P lending platforms.

scholarly journals Evaluating Default Risk and Loan Performance in UK Peer-to-Peer Lending: Evidence from Funding Circle

2021 ◽  
Vol 25 (5) ◽  
pp. 530-538
Author(s):  
Boyu Xu ◽  
◽  
Zhifang Su ◽  
Jan Celler
Keyword(s):  
Interest Rate ◽  
Default Risk ◽  
Binary Logistic Regression ◽  
The United States ◽  
Peer To Peer ◽  
Loan Default ◽  
P2p Lending ◽  
Loan Defaults ◽  
The Interest Rate ◽  
The Uk

The United Kingdom is the third-largest peer-to-peer (P2P) lending market in the world, which is surpassed only by the two dominant forces in P2P investing, China and the United States of America. As an innovative financial market in the UK, P2P lending brings not only many opportunities but also many risks, especially the loan default risk. In this context, this paper uses binary logistic regression and survival analysis to evaluate default risk and loan performance in UK P2P lending. The empirical results indicate that credit group, loan purpose for capital needs, sector type, loan amount, interest rate, loan term, and the age of the company all have a significant impact on the probability of loan default. Among them, the interest rate, loan term, and loan purpose for capital needs are the three most important determinants of the probability of loan defaults and survival time of loans.

scholarly journals The Effect of Digital Unsecured Loans and DTI Ratio on Risk-Taking Behavior

2019 ◽  
Vol 5 (2) ◽  
pp. 133-136
Author(s):  
Fachmi Pachlevi ◽  
Sopacua, Ivana Oktarina
Keyword(s):  
Interest Rate ◽  
Factorial Design ◽  
Internal Control ◽  
Risk Taking ◽  
High Interest Rate ◽  
High Interest ◽  
Increased Risk ◽  
P2p Lending ◽  
Changes In Risk ◽  
Risk Taking Behavior

The objective of this study was to examined the effect of digital unsecured loans and DTI ratio on changes in risk-taking behavior of the household sectros. Increasing of P2P lending is clearly unstoppable in Indonesia. Digital unsecured loans success to simplify credit process, because online-based credit aplication. However, these simply process are followed by high-interest rate. Many people apply for credit without considering risk. The convenience of digital unsecured loans making people forget about high annual percentage rate. Finally, occur increase potential bad loans in the household sectros. Collection of data was carried out through experiments 2 x 2 factorial design. The results shows that digital unsecured loans increases risk-taking behavior of household sectors. DTI ratio also can be used as an internal control of household sectors to prevent increased risk-taking behavior

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