Description-text related soft information in peer-to-peer lending – Evidence from two leading European platforms

Dorfleitner, Gregor; Priberny, Christopher; Schuster, Stephanie A.; Stoiber, J.; Weber, Martina; Castro, Ivan de; Kammler, Julia

doi:10.1016/j.jbankfin.2015.11.009

Cited by 261 publications

(137 citation statements)

References 30 publications

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“…In addition, Dorfleitner et al (2016) study the relation between the soft information derived from the listing description and the lending outcomes based on data from two leading European P2P platforms. They find that spelling errors, text length and the presence of social and emotional keywords in the description text significantly negatively influence the likelihood of funding success, but are not significantly related to the default probability.…”

Section: Related Literature Reviewmentioning

confidence: 99%

“…Some investors may favour credit bids on certain special loan categories, leading to a higher probability of a successful funding and a higher interest rate (Lin et al, 2013;Dorfleitner et al, 2016). We classify the intended purposes of loans into seven categories: debt consolidation, buying a house, bill payments, business loans, automobile loans, home improvement loans, medical loans, education loans, wedding loans and other loans.…”

Section: Data and Variablesmentioning

confidence: 99%

“…In addition, we include dummy variables to indicate the borrower's employment status (EMP) and employment length (EMPYEAR); these factors can indirectly convey information about his or her financial situation and debt-servicing capabilities while also influencing both funding success and interest rate (see Iyer et al, 2016). Finally, following Dorfleitner et al (2016), we include an additional control variable LTEXT, which comprises the number of words included to measure the length of the descriptive text.…”

Section: Data and Variablesmentioning

confidence: 99%

“…Herzenstein and Andrews, 2008;Iyer et al, 2009;Dorfleitner et al, 2016) and friendship and group networks (e.g. Chen et al, 2009;Lin, 2013;Liu et al, 2015;Bellefamme et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Who can get money? Evidence from the Chinese peer-to-peer lending platform

2017

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This paper explores how borrowers' financial and personal information, loan characteristics and lending models affect peer-to-peer (P2P) loan funding outcomes. Using a large sample of listings from one of the largest Chinese online P2P lending platforms, we find that those borrowers earning a higher income or who own a car are more likely to receive a loan, pay lower interest rates, and are less likely to default. The credit grade assigned by the lending platform may not represent the creditworthiness of potential borrowers. We also find that the unique offline process in the Chinese P2P online lending platform exerts significant influence on the lending decision. We discuss the implications of our results for the design of big databased lending markets.

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Section: Related Literature Reviewmentioning

confidence: 99%

Section: Data and Variablesmentioning

confidence: 99%

Section: Data and Variablesmentioning

confidence: 99%

“…Herzenstein and Andrews, 2008;Iyer et al, 2009;Dorfleitner et al, 2016) and friendship and group networks (e.g. Chen et al, 2009;Lin, 2013;Liu et al, 2015;Bellefamme et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Who can get money? Evidence from the Chinese peer-to-peer lending platform

2017

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“…A study of two European P2P lending platforms suggests that investors are perceptive to the lexical signals. The study found that loan requests containing spelling errors are less likely to receive funding [14].…”

Section: Crowdfunding-related Researchmentioning

confidence: 91%

An Exploratory Analysis of Title II Crowdfunding Success

Mamonov

Malaga

Rosenblum³

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Predicting loan default in peer‐to‐peer lending using narrative data

Xia

et al. 2019

Journal of Forecasting

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Peer-to-peer (P2P) lending is facing severe information asymmetry problems and depends highly on the internal credit scoring system. This paper provides a novel credit scoring model, which forecasts the probability of default for each applicant and guides the lenders' decision-making in P2P lending. The proposal is expected to improve the existing credit scoring models in P2P lending from two aspects, namely the classifier and the usage of narrative data. We utilize an advanced gradient boosting decision tree technique (i.e., CatBoost) to predict default loans. Moreover, a soft information extraction technique based on keyword clustering is developed to compensate for the insufficient hard credit data. Validated on three real-world datasets, the experimental results demonstrate that variables extracted from narrative data are powerful features, and the utilization of narrative data significantly improves the predictability relative to solely using hard information. The results of sensitivity analysis reveal that CatBoost outperforms the industry benchmark under different cluster numbers of extracted soft information; meanwhile a small number of clusters (e.g., three) is preferred for consideration of model performance, computational cost, and comprehensibility. We finally facilitate a discussion on practical implication and explanatory considerations.

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Description-text related soft information in peer-to-peer lending – Evidence from two leading European platforms

Cited by 261 publications

References 30 publications

Who can get money? Evidence from the Chinese peer-to-peer lending platform

Who can get money? Evidence from the Chinese peer-to-peer lending platform

An Exploratory Analysis of Title II Crowdfunding Success

Predicting loan default in peer‐to‐peer lending using narrative data

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