Social collateral, soft information and online peer-to-peer lending: A theoretical model

Liu, Zhengchi; Shang, Jennifer; Wu, Shin Yi; Chen, Pei yu

doi:10.1016/j.ejor.2019.08.038

Cited by 49 publications

(26 citation statements)

References 30 publications

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“…P2P is called person-to-person (partner-topartner) and also known as peer-to-peer online lending [9]. The P2P lending model is a private small-value lending model that collects a small amount of funds and then lends them to borrowers with capital needs [10]. This kind of financial service is a financial act of borrowing through the online credit platform of mobile internet technology.…”

Section: Introductionmentioning

confidence: 99%

Credit Risk Management of P2P Network Lending

Ding

et al. 2021

Teh. vjesn.

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This article first studies the literature of P2P online loans, including online loans, credit risk factors and models, and summarizes the current status of P2P and credit risk assessment management in China. Based on the loan data of domestic P2P lending platforms, this paper conducts an empirical study on credit risk assessment. This study uses random forest importance assessment and logistic regression classification for credit risk assessment to identify loan targets with higher probability of default and improve overall loan quality. This research used 10,930 loan data, based on 26 fields, and finally selected 20 model variables to participate in credit risk quantification through feature structure and feature analysis. The final modelling test results show that the model screening accuracy rate is 73.3%, indicating that this model has a good performance in the credit risk quantification of borrowers.

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Section: Introductionmentioning

confidence: 99%

Credit Risk Management of P2P Network Lending

Ding

et al. 2021

Teh. vjesn.

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“…Peer to Peer (P2P) network lending is a widely-developed and typical model of Internet finance. It connects lenders and lenders through the Internet and helps the two parties to establish lending relationships and complete a series of transaction procedures through the network platform [5]- [7]. On this platform, the borrower publishes personal information and loan information, and the investor can decide whether to invest the idle funds at hand according to the borrower's personal information, loan information, and fund use information, thereby realizing the financial communication.…”

Section: Introductionmentioning

confidence: 99%

FCM-Based P2P Network Lending Platform Credit Risk Dynamic Assessment

et al. 2020

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With the rapid development of Internet finance, Peer to Peer(P2P) network lending has also developed as a new type of financing. However, as the P2P network lending platform becomes more and more popular, problems such as running, shutting down, and withdrawing cash are emerging. One of the most common problems is caused by credit. This paper addresses the issues such as credit problems and existing risks of online platforms by proposing a fuzzy cognitive map (FCM) that is used to establish a credit risk assessment model for the P2P network lending platform. It provides a new idea for the credit risk assessment of online lending platforms. The paper uses Lu Jinfu as an example to evaluate its credit risk and selects two other platforms to rank its credit risk. The results show that the fuzzy cognitive map considers the mutual influence and feedback between the indicators. The index weight matrix learned by historical data represents the relationship between the indicators. After inputting the initial data into the inference, the index data reaches a steady state. The steady-state refers to the state that we expect the indicator to reach after a certain period. As an objective evaluating method for the credit risk of the P2P network lending platform, the proposed method has demonstrated its advantage and effectiveness.

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“…al., 2016). While platforms might be more agile in using new financial and information technologies (Liu et al, 2020) and be more proactive in using alternative data, incumbent banks might have informational advantage due to the private information obtained during past lending relationships. 2 Moreover, incumbent banks enjoy implicit government subsidies that allow them to have lower cost of funding in comparison to platforms (Havrylchyk, 2018).…”

Section: Introductionmentioning

confidence: 99%

Real Effects of Lending-Based Crowdfunding Platforms on the SMEs

Havrylchyk

Ardekani

2020

SSRN Journal

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This paper explores the short-term impact of borrowing via lending-based crowdfunding on performance and health of small and medium enterprises (SMEs) in France. We find that firms borrowing from lending-based crowdfunding platforms are more dynamic (higher asset growth and higher profitability) and innovative, but they have lower leverage, less cash, higher funding costs and less tangible assets that could be pledged as a collateral. To account for this selection bias, we construct three control groups by using Propensity Score Matching, Mahalanobis Distance Matching and Coarsened Exact Matching methods and then run difference-indifference regressions. We find that borrowing via lending-based crowdfunding platforms increases SMEs' leverage and interest rate burden in the short-term, but these impacts disappear after two years. We observe asset growth during the year of borrowing, but no impact on sales growth, investment, employment or profitability.

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Social collateral, soft information and online peer-to-peer lending: A theoretical model

Cited by 49 publications

References 30 publications

Credit Risk Management of P2P Network Lending

Credit Risk Management of P2P Network Lending

FCM-Based P2P Network Lending Platform Credit Risk Dynamic Assessment

Real Effects of Lending-Based Crowdfunding Platforms on the SMEs

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