A Joint Scoring Model for Peer-to-Peer and Traditional Lending: A Bivariate Model with Copula Dependence

Calabrese, Raffaella; Osmetti, Silvia Angela; Zanin, Luca

doi:10.1111/rssa.12523

Cited by 12 publications

(6 citation statements)

References 49 publications

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“…Further, it is a well-known fact that better sources of information are more useful than better models when it comes to making predictions. This is discussed at length in Baesens et al (2016) and shown empirically for newer so-called alternative data in a peer-to-peer and retail credit risk environment by Calabrese et al (2019) and Óskarsdóttir et al (2019).…”

Section: Measuring Credit Risk In the Agribusiness Sectormentioning

confidence: 94%

The effects of customer segmentation, borrower behaviors and analytical methods on the performance of credit scoring models in the agribusiness sector

2020

JCR

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The main aim of this study is to analyze the joint effects of customer segmentation, borrower characteristics and modeling techniques on the classification accuracy of a scoring model for agribusinesses. To this end, we used data provided by a Chilean company on 161 163 loans from January 2007 to December 2013. We considered random forest, neural network and logistic regression models as analytical methods. Regarding borrowers' profiles, we examined the effects of sociodemographic, repayment behavior, agribusiness-specific and credit-related variables. We also segmented the customers as individuals, small and medium-sized enterprises, and large holdings. As the segments show different risk behaviors, we obtained a better performance when we estimated a scoring model for each segment instead of using a

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Section: Measuring Credit Risk In the Agribusiness Sectormentioning

confidence: 94%

The effects of customer segmentation, borrower behaviors and analytical methods on the performance of credit scoring models in the agribusiness sector

2020

JCR

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“…In 2014, the P2P lending generated approximately billion worth of loans in the USA according to PricewaterhouseCoopers (2015), and the market may reach billion or higher by 2025 (Calabrese et al. 2019 ). There is a risk, however, that loans and interest may not be repaid due to information asymmetry between borrowers and lenders, which can lead to adverse selection and moral hazard problems (Lin et al.…”

Section: Introductionmentioning

confidence: 99%

Default risk prediction and feature extraction using a penalized deep neural network

et al. 2022

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Online peer-to-peer lending platforms provide loans directly from lenders to borrowers without passing through traditional financial institutions. For lenders on these platforms to avoid loss, it is crucial that they accurately assess default risk so that they can make appropriate decisions. In this study, we develop a penalized deep learning model to predict default risk based on survival data. As opposed to simply predicting whether default will occur, we focus on predicting the probability of default over time. Moreover, by adding an additional one-to-one layer in the neural network, we achieve feature selection and estimation simultaneously by incorporating an -penalty into the objective function. The minibatch gradient descent algorithm makes it possible to handle massive data. An analysis of a real-world loan data and simulations demonstrate the model’s competitive practical performance, which suggests favorable potential applications in peer-to-peer lending platforms.

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“…Zanin 2017Zanin , 2018a, risk management (e.g. Calabrese et al 2019), medicine (e.g. Winkelmann 2012;Espasandín-Domínguez et al 2019), management science (e.g.…”

Section: Introductionmentioning

confidence: 99%

Determinants of the Choice of Job Search Channels by the Unemployed Using a Multivariate Probit Model

Mussida

Zanin

2020

Soc Indic Res

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We investigate factors affecting the job search channels (newspapers, agency, informal network, direct contact, internet) of unemployed individuals in Italy using a multivariate probit model. We separately analyse unemployed individuals who lost their job or who are entering the labour market for the first time (i.e., with or without previous job experience, respectively). We use cross-sectional microdata covering the 2014-2018 period. We do not find important differences in the determinants in common between unemployed individuals with and without previous job experience (such as, age, education, and citizenship). The main difference between the two samples is in the composition, with more young people in the latter group (mainly individuals in the school-to-work transition) than in the first one. We find that better-educated unemployed people have more of a multichannel attitude when searching for jobs than their counterparts. Moreover, for unemployed individuals with previous job experience, the characteristics of their last job play a crucial role in influencing the choice of search channel(s). For example, unemployed individuals from highskilled professions (that typically require the employment of better-educated individuals) choose the internet with a higher probability than other channels, while those from lowskilled professions and with experience in manufacturing have a higher probability of considering employment agencies than those in qualified professions in commercial activities and services. We have assumed that the unobservable factors affecting the decision process of a multichannel job search (estimated through the pairwise correlation coefficients across the residuals of the models) are represented by personality features of an individual, such as motivation, self-control, and preferences. A strong, positive, and significant correlation is found between job searching through the internet and direct contact with firms, but with some spatial differences between macro-areas of the country.

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A Joint Scoring Model for Peer-to-Peer and Traditional Lending: A Bivariate Model with Copula Dependence

Cited by 12 publications

References 49 publications

The effects of customer segmentation, borrower behaviors and analytical methods on the performance of credit scoring models in the agribusiness sector

The effects of customer segmentation, borrower behaviors and analytical methods on the performance of credit scoring models in the agribusiness sector

Default risk prediction and feature extraction using a penalized deep neural network

Determinants of the Choice of Job Search Channels by the Unemployed Using a Multivariate Probit Model

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