P2P Lending Research Articles

Liquidity risk in FinTech lending: Early impact of the COVID-19 pandemic on the P2P lending market

This study empirically investigates the impact of the COVID-19 pandemic on the liquidity risk incurred by the peer-to-peer (P2P) lending market. As the pandemic adversely affects financial markets globally, a better understanding of the dynamics of successful P2P lending is necessary under the conditions of financial distress. By using the cross-country database of secondary market listing outcomes at Bondora (Estonia) and employing probit, ordered probit and tobit regression methods, we provide evidence of the pandemic-induced exposure to liquidity risk in the P2P lending market. Despite increased volatility in the financial markets, results show that COVID-19 risk increases the probability of successful listing during the pandemic. However, this outcome comes at significant liquidation costs for investors in the form of higher premiums. Further analysis based on listing outcomes and loan characteristics shows a negative association between COVID-19 risk and share of overdue loans and average overdue days in secondary market listings. Simultaneously, more experienced investors dominate the market as COVID-19 risk increases, a trend that is reflected in shorter listing times. The findings of this study imply certain early tendencies in financial markets during pandemic-induced turmoil and open new avenues for further research.

The Influence of Perceived Risk and Perceived Benefits on Continuance Intention to Adopt Fintech P2P Lending Mediated by Trust in Indonesia

This study investigates the influence of perceived risk and perceived benefits on the continuance intention to adopt fintech P2P lending platforms in Indonesia, considering trust as a mediating factor. With the fintech sector experiencing rapid expansion, understanding the determinants of user retention is crucial for sustaining growth and ensuring consumer protection under the oversight of regulatory bodies like Otoritas Jasa Keuangan (OJK). A total of 105 participants were recruited for the survey, ensuring a diverse demoghparic representation in terms of age, gender, job, origin, and job occupation. The data were analyzed using Structrual Equation Modeling (SEM). The anaylis of the data revealed that perceived risk significantly gives negative influenced to continuance intention to adopt fintech p2p lending. In contrast, perceived benefit had a robust positive effect on continuance intention to adopt fintech p2p lending. Trust was found to partially mediate the relationship between perceived risk, perceived benefits, and continuance intention.

Predicting failure of P2P lending platforms through machine learning: The case in China

This study employs machine learning models to predict the failure of Peer-to-Peer (P2P) lending platforms, specifically in China. By employing the filter method and wrapper method with forward selection and backward elimination, we establish a rigorous and practical procedure that ensures the robustness and importance of variables in predicting platform failures. The research identifies a set of robust variables that consistently appear in the feature subsets across different selection methods and models, suggesting their reliability and relevance in predicting platform failures. The study highlights that reducing the number of variables in the feature subset leads to an increase in the false acceptance rate while the performance metrics remain stable, with an AUC value of approximately 0.96 and an F1 score of around 0.88. The findings of this research provide significant practical implications for regulatory authorities and investors operating in the Chinese P2P lending industry.

Discovering Latent Macroeconomic Effects in Peer-to-Peer Lending Data

Peer-to-peer (P2P) lending is a fast growing financial technology (FinTech) trend that is displacing traditional retail banking. Studies on P2P lending have focused on predicting individual interest rates or default probabilities. However, the relationship between aggregated P2P interest rates and the general economy will be of interest to investors and borrowers as the P2P credit market matures. We show that the variation in P2P interest rates across grade types is determined by three macroeconomic latent factors formed by Canonical Correlation Analysis (CCA)—macro default, investor uncertainty, and the fundamental value of the market. However, the variation in P2P interest rates across term types cannot be explained by the general economy.

Politeness matters: The role of polite languages in online peer-to-peer lending

Given the limited information for assessing borrowers in online peer-to-peer (P2P) lending, the textual content of loan requests has been recognized as a crucial factor influencing lenders' lending decisions. However, whether politeness, an important but largely neglected linguistic element in loan requests, is related to lenders' decisions remains underexplored. Drawing from signaling theory, this paper seeks to answer two important questions: (1) Is the use of polite language in loan requests related to lenders' decisions? (2) If so, for which types of borrowers or loans is politeness more beneficial? We test our theoretical framework using a sample of 127,044 loan requests from Renrendai, a leading online P2P lending platform in China. Our findings reveal a significant and positive relation between the use of polite language and the likelihood of successful funding. This positive relation is stronger for borrowers with higher credit ratings, higher education levels, as well as for loan requests with lower information richness and higher certainty in language. Our findings suggest that politeness functions as a costless signal that complements costly signals, reducing information asymmetry in P2P lending. This paper bridges the gap between the literature on the role of politeness in individual decision making and information asymmetry in financial markets, providing new insights into the role of language use in P2P lending.

A study on lender participation in peer to peer lending

PurposeThe purpose of this study is to examine the transformation expectations and well-being of lenders as a consequence of participation in peer-to-peer (p2p) lending in India. The study tested the influence of transformation expectations on well-being, social connectedness on transformation expectations and the influence of customer participation on financial empowerment of borrowers.Design/methodology/approachA cross-sectional survey research design was adopted to collect data from 434 customers in India. Structural equation modelling procedure was performed to test the hypothesized relationships.FindingsThe findings show that relational transformation expectations leads to well-being, while social connectedness influences self and relational transformation expectations. Further, customer participation has a positive influence on financial empowerment of borrowers.Practical implicationsManagers may use these findings to create suitable marketing strategies for increasing customer participation in p2p lending.Originality/valuePrevious studies on transformative services have shown that well-being can be achieved through participation in services that are transformative by design, while this study has established that lender participation in peer-to-peer lending can result in relational transformation expectations and lead to well-being. Also, the current study has shown that social connectedness is an antecedent of transformation expectations.

Exploring FinTech Lending: The Influence of Financing and Economic Factors on the Success of Peer-to-Peer (P2P) Funded Loans

This research intends to integrate financing and economic factors into a unified model to explore their influence on P2P lending successful fundraising. An innovative framework of the ARDL method was utilized to explore the short and long-term effects of the factors model to allow the P2P lending players to make informed decisions and develop effective strategies for sustainable FinTech lending practices. Information asymmetry is a persistent concern, and the quality of information provided by both internal and external sources plays a crucial role in determining lender decisions. It aims to offer a holistic understanding of how these elements interact and collectively influence P2P lending loan success, emphasizing the importance of transparency and efficient online lending practices.

Financial Technology and Risk Management

This essay gives a general overview of how fintech has impacted the financial services sector, highlighting the significance of technologies like blockchain, Robo-Advisors, online payment, and P2P lending. The essay examines how blockchain technology affects financial transactions, including its potential to replace conventional middlemen and improve security and transparency. The rise of Robo-counselors as a disruptive technology that gives investors a cheap and practical substitute for traditional financial counselors is also covered. The article emphasizes the significance of striking a balance between technology and human competence by contrasting the benefits and drawbacks of robot advisors with those of actual advisors. Furthermore, the essay covers the expansion of online payment options, such as digital wallets and mobile payment applications, and how these may change how customers make purchases. Finally, the article explores the potential and hazards raised by the emergence of peer-to-peer lending platforms, which give borrowers an alternative to conventional bank loans. Overall, the article underlines how fintech has the potential to revolutionize the financial services sector, but it also stresses how crucial it is to strike a balance between innovation, financial stability, and regulatory compliance.

Determinants of firms’ default on unsecured loans in the P2P crowdfunding market

We investigate the payback performance of small businesses’ funded loans, and its determinants, in the Peer-to-Peer (P2P) lending crowdfunding market. We do so by analysing a dataset of 1000 small businesses’ unsecured funded loans, via the American online P2P lending funding website Prosper.com. As these loans are unsecured, interest rates are relatively high (18.5% on average in our data base). As a result, payback is by no means straightforward. Indeed, we find 27% of all funded loans in our sample to default. Hence, it is particularly important in this market to know which factors determine payback performance. A novelty of our paper is that we are able to distinguish empirically between new and established firms in this market. Our results highlight that also in P2P lending, borrower reputation — stipulated by credit grade and repayment history — continues to be the single most dominant determinant of payback over loan default, especially for established firms. However, in contrast to conventional financing theory, we found that borrowers’ credit grade is only weakly relevant in determining risks of default of new businesses and that new firms are not more likely to default when compared to established firms. Selection effects may play a role here, in that only high-risk established firms look for unsecured online P2P finance as they are more likely than lower risk established firms to have been unable to get finance in the more traditional markets. This suggests that the liability of newness does not hold in this market. Nevertheless, among new firms, and in contrast to established firms, inclusion of a picture in the loan application was found to be an additional quality signal, positively associated with payback performance. This suggests the importance of visual information as a means of reducing uncertainty and signalling lower risk to financiers.

Factors Affecting Operational Performance nad How It Effect Corporate Sustainability (From the Perspective of Fintech P2P Lending Companies in Indonesia)

P2P lending is a direct lending and borrowing service between lenders and borrowers based on information technology platforms. P2P lending fintech companies face various problems related to the quality of business ethics and cyber risk management, which have an impact on operating performance and company sustainability. This study aims to prove empirically whether there is an influence of the business ethics and cyber risk management on operating performance and its implications for the sustainability of P2P lending fintech companies registered and licensed at the OJK. The research data was collected through a questionnaire instrument, which was collected from 104 P2P lending fintech companies registered/licensed at the OJK until the end of December 2021. The data were analyzed using the statistical technique of Structural Equation Modeling (SEM). The results showed that the quality of cyber risk management had a significant positive effect on operating performance. The test results also show that the business ethics and cyber risk management has a significant positive effect on the sustainability of the P2P lending fintech business. However, business ethics has a significant positive effect on operating performance. The findings of this study add to the limitations of the research literature on the elaboration of performance determination and business sustainability in P2P lending fintech.without repeating what has already been done by others.

Enhancing credit risk prediction based on ensemble tree‐based feature transformation and logistic regression

AbstractThe assessment of credit risk for P2P lending platform applicants is critical to investors. Feature engineering is an essential technique in distilling classification knowledge during the credit risk prediction data preprocessing stage. Although previous literature used feature selection methods to identify key features, feature transformation is more useful in discovering intrinsic nonlinear characteristics in credit data. In this study, we propose a synthetic multiple tree‐based feature transformation method to generate features. Multiple tree‐based feature transformation methods are employed and fused to acquire a new feature set. The bagging‐based tree ensemble feature transformation method (Bagging‐TreeEnsembleFT) and boosting‐based tree ensemble feature transformation method (Boosting‐TreeEnsembleFT) are two types of feature transformation methods that we specifically propose to validate their effect. We verify the credit risk prediction performance using the proposed synthetic feature transformation methods on real P2P Lending credit datasets. Empirical analysis demonstrates that tree‐based ensemble feature transformation methods with boosting ensemble strategy achieve better prediction performance on various datasets corresponding to different partitions and class distributions compared to tree‐based ensemble feature transformation methods with bagging ensemble strategy and individuals. Moreover, the proposed synthetic feature transformation method improves the credit risk prediction performance in terms of accuracy, AUC, and F1‐score.

Feature selection in P2P lending based on hybrid genetic algorithm with machine learning

The emergence of online lending services such as peer-to-peer (P2P) lending has simplified transactions for lenders, eliminating the need for traditional bank intermediaries. However, accurately predicting potential lender defaults is a critical challenge in preventing financial distress, as lenders bear the burden of default risk. This challenge becomes even more complicate with P2P lending datasets that feature a multitude of complex attributes. To improve predictive accuracy, the study focuses on optimizing feature selection in this data-rich environment. Its main objective is to identify the most influential features for predicting default risk among P2P lenders through feature optimization techniques, leveraging the Genetic Algorithm (GA) in conjunction with ten different Machine Learning models. The study employed a hybrid approach of GA with three mutation rate levels, basic (MR=0), moderate (MR=0.5), and extreme (MR=1), to provide insights into model responsiveness and performance under various mutation scenarios. Notably, the results highlight GA+XGBoost as the best-performing model with a stable fitness score of 86.132% compared to others. This research holds significant potential for improving lender risk management in the P2P lending landscape by effectively identifying higher risk lenders. Ultimately, the findings contribute positively to the mitigation of financial risks for lenders within the P2P lending ecosystem.

Embracing the digital economy: Exploring the role of trust, perceived ease of use, and religiosity on intention to use Islamic peer-to-peer lending

This study investigates the pivotal roles of trust, perceived ease of use, and religiosity on individuals' intentions to adopt Islamic peer-to-peer (P2P) lending platforms within the digital economy context. The novelty of this study lies in its focus on an area that has received limited attention in prior studies (Islamic P2P lending) and the inclusion of individual factors, particularly religiosity, in the analysis. Additionally, a five-point Likert scale survey and purposive sampling technique were employed, targeting young Muslim potential borrowers aged 18-24 (Generation Z) who are presently engaged in microbusiness activities and have knowledge of financial technology. However, they had not yet utilized any P2P lending platforms. Data from three hundred twenty-three respondents was analyzed to investigate the relationships between variables. The results of this study suggest that all examined variables, including trust, perceived ease of use, and religiosity, have a significant and positive impact on individuals' willingness to use Islamic P2P lending. This implies that the more trust individuals have in Islamic P2P lending platforms, the easier they perceive them to use, and the stronger their religious beliefs, the more likely they are to accept and utilize these new Islamic financial services.

Crowdfunding Sharia Financing Model as Alternative Business Capital Financing for MSMES

In the era of the Industrial Revolution 4.0, there are currently financial product innovations, one of which is fintech (financial technology). Crowdfunding is a fintech product with a Sharia concept, that can be accessed on the internet network. Likewise, it is a form of funding for those who need funds for business development. In the Shariah concept, crowdfunding is a form of work or syirkah where there is a collective effort from several people who come together to finance a business through a digital platform called financial technology. This is a qualitative research, using bibliometric analysis approach. Crowdfunding aims to be an alternative financing, in Indonesia itself. Sharia Fintech had emerged in 2017, the Indonesian Sharia Fintech Association (AFSI) has been formed by the Tazkia Fintech Study Center with members of 8 Sharia fintech companies, the latest data shows that in 2020 the addition of the number of AFSI members consists of 18 members of peer to peer (p2P) lending, 27 members of digital financial innovation (IKD), four members of Sharia equity crowdfunding (ECF), 2 payment members, 2 non-fintech members, and 9 sharia members, and financial partner.
 Keywords: crowdfunding, MSMEs, bibliometric analysis

Exploring the influence of Blockchain in the financial services: Quick assessment of its applications across various financial domains

Blockchain technology has evolved from an enigmatic concept to a pioneering new frontier for our collective digital infrastructure in the last two decades. Its vast potential has transcended industries and possesses the capacity to transform domains as diverse as healthcare and online gaming fundamentally. However, Blockchain technology has thus far demonstrated its most efficient and effective application within the financial sector. The financial industry has benefited significantly by adapting this new technology (Blockchain) in terms of transactional transparency, security, and operational effectiveness. Blockchain is made feasible by developing immutable, decentralized, and distributed ledgers that can be viewed in real-time by numerous parties without intermediaries. As a result, transaction processing is secure and dependable, reducing the likelihood of fraud, errors, and delays. Because of their decentralized nature and smart contract technology, these instruments permit the establishment of novel financial services, such as P2P lending, decentralized insurance, and decentralized asset management. Rapid Blockchain adoption revolutionizes the financial sector, ushering in a new era of innovation and upheaval.

Exploring digital banking adoption in developing Asian economies: Systematic literature review and bibliometric analysis

AbstractConsumers’ comfort and familiarity with digital interactions are helping drive the rise of digital banking. Banks are responding to customers’ demands by introducing cutting‐edge digital services designed to improve customers’ quality of life and cut costs. To better understand the obstacles preventing the widespread adoption of digital banking in developing Asian economies, we conducted a systematic literature review and bibliometric analysis for this study. The data is extracted from the Scopus database, which initially contains 4211 articles, from which 152 articles were selected using PRISMA 2020 criteria for bibliometric analysis using VOSviewer and Biblioshiny software, and 85 papers were subjected to full‐text review. The review, limited to the Scopus database, uncovered essential themes, theories and methodologies used in prior research. We discuss constraints, difficulties and research gaps, illuminating critical regions of the field. Our findings offer helpful information for developing strategies to encourage the use of digital banking. The unexplored areas of crowdfunding, Robo‐advisors, payment apps, neo banks, P2P lending apps and investing apps were highlighted as future research directions.

Exploring the spatial linkage network of peer-to-peer lending in China

The existing literature on peer-to-peer (P2P) lending focuses on default rates, regulation, the relationship with traditional banks, and its impact on individual and corporate lending behavior, but generally ignores its spatial network characteristics. By extracting the spatial location information from the transaction data of Renrendai, this study constructs the spatial linkage network of P2P lending from 2011 to 2015 and employs social network analysis methods to study the evolution characteristics of the network and its driving factors. The main conclusions are as follows: (1) The spatial linkage network of P2P lending went through a process of expansion to contraction, with a decrease in the number of nodes and linkages and a trend of spatial fragmentation in network communities since 2013. (2) There are differences in the evolutionary driving factors for the formation and dissolution of network relationships. The formation of network relationships is based on variables such as the demand for P2P lending and city size, while the dissolution of network relationships is based on the spread of risks and the ability to resist risks. (3) The results demonstrate that proximities differ in their relative importance for linkage formation and dissolution. Geographic proximity plays a significant role in both models, but cultural proximity and inter-city accessibility only matter in the formation model.

Relationship Between Interest Rate and Risk of P2P Lending in China Based on the Skew-Normal Panel Data Model

This study examines the relationship between interest rate and defunct platform risk of China’s peer-to-peer (P2P) lending platforms. P2P lending provides an alternative funding source for individuals and micro-enterprises and offers a new investment tool for households. But the frequent collapses of many platforms were huge losses to market participants and even led to a decline in China’s P2P lending industry. In this study, weekly data of 76 platforms from December 3, 2017, to October 6, 2019, are employed, and empirical research based on the normal and skew-normal panel data model respectively is conducted. Statistical indicators prove that the skew-normal panel data model is preferable to another one in modeling the data set of interest rates. The empirical results show that China’s P2P market is efficient overall. But the positive correlation between the interest rate and risk is not significant for platforms with excessively high interest rates, whose interest rates are more determined by the types of ownership. The findings and implications are provided in the end.

Internet of Things and Blockchain-Based Smart Contracts: Enabling Continuous Risk Monitoring and Assessment in Peer-to-Peer Lending

ABSTRACT Peer-to-peer (P2P) lending enables individuals and small companies to finance and invest without the intermediation of financial institutions. However, this business model is also associated with high delinquency risk and a lack of risk monitoring and control capabilities. This paper explores the potential of the Internet of Things (IoT), blockchain, smart contract technologies, and the Continuous Risk Monitoring and Assessment (CRMA) framework to re-engineer risk monitoring and control for P2P lending. We conducted a case study of a large Chinese P2P lending company to identify problems in its current risk monitoring and control processes and to design an IoT-smart contract CRMA system to continuously monitor and respond to delinquency risk via real-time data collection, automatic loan settlement, and in-time risk disclosure. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M40; M41; M49.

Credit Risk Detection in Peer-to-Peer Lending Using CatBoost

P2P lending (Peer-to-peer lending) is widely used by private borrowers, small businesses, and MSMEs because P2P lending allows individuals and businesses to be able to lend money directly from lenders without the stringent requirements and criteria of traditional banks and financial institutions. However, P2P lending has a credit risk problem characterized by a high failure rate for borrowers to repay their loans. Therefore, a system was necessary to detect credit risk to minimize the risk of P2P lending. In this study, a system had been built using the CatBoost method; the dataset used was taken from the Bondora loan dataset. To measure the performance of the CatBoost algorithm, an evaluation matrix was performed using ROC (Receiver Operating Characteristics) curves and AUC (Area Under Curve) was performed. The experiment consists of three scenarios, of which the best result regards Scenario 2 with a data splitting of 90:10. It was caused by the result of AUC value 0.80329 compared to scenario 1 with a data split of 80:20 with the AUC value around 0.789583, and scenario 3 with a data split of 70:30 with the AUC value around 0.781066, respectively.