Research on Credit Risk Assessment of P2P Network Platform: Based on the Logistic Regression Model of Evidence Weight

As an emerging credit model, P2P network credit has been developing rapidly in recent years. At the same time, it also faces many credit risk problems. This paper focuses on the credit risk of borrowers, and constructs a model of WOE and logistic regression to evaluate the risk assessment of China’s P2P network platform, Hong ling Venture. The research results show that the main factors that affect the loan success rate of P2P lending platform include loan amount, annual interest rate, bidding transaction amount and proportion of repayment on time and so on. By constructing the model of combination of the logistic regression with weight of evidence, this paper provides an appropriate method to manipulate the borrowing information of loan borrowers and evaluates the borrowing behavior of borrowers simultaneously, so that P2P credit platform can reduce the credit risk caused by borrower

Peer-to-Peer (P2P) Lending in Europe: Evaluating the Default Risk of Borrowers in the Context of Gender and Education

In recent years, the importance of social lending activities and their effects on consumers have been highlighted by the widespread use of peer-to-peer lending platforms and the global race in fintech. Our study focuses on factors that affect the likelihood that European borrowers on peer-to-peer lending platforms, which are currently based in Estonia, Finland, and Spain, will default on their loans. Starting with the publicly accessible Bondora database, we examine the different economic and social characteristics of the borrowers to analyze the factors that contributed to loan default between 2013 and 2021. We use a Logit model to calculate the ex-post probability of default for factors derived from Principal Component Analysis as well as the original variables supplied by the database. The results show how crucially important education is for borrowers in lowering the risk of default, along with loan characteristics like high debt levels, long loan terms, and high interest rates. In addition, gender plays an important role in determining loan default, with a particular focus on women's conditions within the family. Regarding financial inclusion and its social implications, our findings suggest different ways to improve financial literacy and promote peer-to-peer lending. Future research could develop on the findings by applying them to other lending platforms and countries

On the Improvement of Default Forecast Through Textual Analysis

Textual analysis is a widely used methodology in several research areas. In this paper we apply textual analysis to augment the conventional set of account defaults drivers with new text based variables. Through the employment of ad hoc dictionaries and distance measures we are able to classify each account transaction into qualitative macro-categories. The aim is to classify bank account users into different client profiles and verify whether they can act as effective predictors of default through supervised classification models

Crowdlending: mapping the core literature and research frontiers

[EN] Peer-to-peer (P2P) lending uses two-sided platforms to link borrowers with a crowd of lenders. Despite considerable diversity in crowdlending research, studies in this area typically focus on several common research topics, including information asymmetries, social capital, communication channels, and rating-based models. This young research field is still expanding. However, its importance has increased considerably since 2018. This rise in importance suggests that P2P lending may offer a promising new scientific research field. This paper presents a bibliometric study based on keyword co-occurrence, author and reference co-citations, and bibliographic coupling. The paper thus maps the key features of P2P lending research. Although many of the most cited papers are purely financial, some focus on behavioral finance. The trend in this field is toward innovative finance based on new technologies. The conclusions of this study provide valuable insight for researchers, managers, and policymakers to understand the current and future status of this field. The variables that affect new financial contexts and the strategies that promote technology-based financial environments must be investigated in the future.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Ribeiro-Navarrete, S.; Piñeiro-Chousa, J.; López-Cabarcos, MÁ.; Palacios Marqués, D. (2022). Crowdlending: mapping the core literature and research frontiers. Review of Managerial Science. 16(8):2381-2411. https://doi.org/10.1007/s11846-021-00491-82381241116

The Role of Gender and Education in Peer-to-peer Lending Activities: Evidence from a European Cross-country Study

The wide use of peer-to-peer lending platforms coupled with the Fintech global race has emphasized the role of social lending activities and their impact on consumers in recent years. Starting from the publicly available Bondora database, we analyse determinants of loan default during the 2013-2021 period by studying individual economic and social factors of borrowers. We apply a Logit model to estimate the ex-post probability of default on both original variables provided by the database and factors obtained by Principal Component Analysis. Results show the fundamental role of borrowers’ education in reducing the probability of default, as with financial awareness obtained by loan characteristics. In addition, gender plays an important role in determining loan default, with a particular focus on women's conditions within the family. Regarding financial inclusion and its social implications, our findings suggest different ways to improve financial literacy and promote peer-to-peer lending

Does Borrower Domicile Influence the Credit Default in P2P Lending? Preliminary Analysis from Indonesia

Purpose: Credit risk is one of the most fundamental risks that P2P lending platforms have. The magnitude of information asymmetry, consumer behavior, and the unequal distribution of financial literacy make credit risk in P2P lending more vulnerable in several parts of Indonesia. The purpose of this study was to determine the domicile of the borrower on the credit risk in P2P lending Methodology: We use time series data from January 2018-December 2021 for analysis. Vector Error Correction Model (VECM) is used to analyze the data. Findings: The results show that borrowers domiciled outside Java influence the credit default significantly positively, while borrowers domiciled in Java influence credit default significantly negatively. Moreover, interest rate influences positively significant on P2P lending default, while inflation influences positively on P2P lending default. Novelty: this paper is the first paper to analyze the P2P credit default in Indonesia using time series analysis.

Crowdlending: mapping the core literature and research frontiers

Peer-to-peer (P2P) lending uses two-sided platforms to link borrowers with a crowd of lenders. Despite considerable diversity in crowdlending research, studies in this area typically focus on several common research topics, including information asymmetries, social capital, communication channels, and rating-based models. This young research field is still expanding. However, its importance has increased considerably since 2018. This rise in importance suggests that P2P lending may offer a promising new scientific research field. This paper presents a bibliometric study based on keyword co-occurrence, author and reference co-citations, and bibliographic coupling. The paper thus maps the key features of P2P lending research. Although many of the most cited papers are purely financial, some focus on behavioral finance. The trend in this field is toward innovative finance based on new technologies. The conclusions of this study provide valuable insight for researchers, managers, and policymakers to understand the current and future status of this field. The variables that affect new financial contexts and the strategies that promote technology-based financial environments must be investigated in the futureOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer NatureS

The Role of Gender and Education in Peer-to-peer Lending Activities: Evidence from a European Cross-country Study

The wide use of peer-to-peer lending platforms coupled with the Fintech global race has emphasized the role of social lending activities and their impact on consumers in recent years. Starting from the publicly available Bondora database, we analyse determinants of loan default during the 2013-2021 period by studying individual economic and social factors of borrowers. We apply a Logit model to estimate the ex-post probability of default on both original variables provided by the database and factors obtained by Principal Component Analysis. Results show the fundamental role of borrowers’ education in reducing the probability of default, as with financial awareness obtained by loan characteristics. In addition, gender plays an important role in determining loan default, with a particular focus on women's conditions within the family. Regarding financial inclusion and its social implications, our findings suggest different ways to improve financial literacy and promote peer-to-peer lending

Network centrality and credit risk:A comprehensive analysis of peer-to-peer lending dynamics

This letter analyzes credit risk assessment in the Peer-to-Peer (P2P) lending domain by leveraging a comprehensive dataset from Bondora, a leading European P2P platform. Through combining traditional credit features with network topological features, namely the degree centrality, we showcase the crucial role of a borrower's position and connectivity within the P2P network in determining loan default probabilities. Our findings are bolstered by robustness checks using shuffled centrality features, which further underscore the significance of integrating both financial and network attributes in credit risk evaluation. Our results shed new light on credit risk determinants in P2P lending and benefit investors in capturing inherent information from P2P loan networks.</p