P2P Lending Platforms Research Articles

Borrower switching behaviour on a P2P lending platform: a study of switching path analysis technique

Borrower switching behaviour on a P2P lending platform: a study of switching path analysis technique

Estimating probability of default via delinquencies? Evidence from European P2P lending market

The unprecedented growth of the financial sector's digital transformation opens wide areas to the scaling up of finance in innovative and knowledge-based projects. Improving risk management takes centre stage in the acceleration of this process. This study uses loan-book data from the peer-to-peer (P2P) lending market to empirically investigate the determinants of default risk. Using the loan-book database covering the period from 2014 to 2020, we examine multiple factors related to the default risk of loans issued by P2P lending platforms. The results indicate that a higher interest rate and higher stock market returns increase the probability of default in the P2P lending market. Results are robust to additional tests based on endogeneity correction, the LASSO method and sampling bias. The severity of the impact of market returns and interest rates is found to be significantly different based on the levels of financial technology (FinTech) adoption and banking sector distress. Increases in the market interest rate are found to boost the sensitivity of P2P loan defaults to stock market volatility. This study contributes to existing literature on risk management models with its consideration of country-specific factors, paving the way to future best practices in the market.

Do P2P borrowers improve the quality of information disclosure? An analysis with text mining on loan descriptions

AbstractMost of peer‐to‐peer (P2P) online borrowers are small business managers. The learning behavior of borrowers in the P2P market is rarely studied. The aim of this paper is to identify the existence of borrowers' learning behavior in the P2P market using a large sample from renrendai.com, which is one of the largest P2P lending platforms in China. The loan description written by the borrower is an important way to disclose the borrower's information. We analyze changes in loan descriptions in multiple borrowings with text mining techniques and investigate whether a borrower has a learning behavior in writing loan descriptions. Empirical results show that after accumulating enough experience, borrowers can optimize the loan description to make it easier to obtain loans at lower interest rates.

Keputusan Pemberian Kredit Kepada UMKM Dalam Peer-To-Peer Lending: Faktor-Faktor Apa Saja Yang Dipertimbangkan Investor?

This study aims to examine and demonstrate six factors influencing the decision to grant credit loans to MSMEs on the conventional Peer-to-peer (P2P) lending platforms Investree and Koinworks. These six factors are loan amount, loan term, credit rating, loan purpose, business duration, and financial statement information. A quantitative approach and secondary data from the publications of the Investree and Koinworks applications or websites were utilized in this research. A sample of 225 MSMEs was selected from a population of 267 using purposive sampling. Logistic regression analysis revealed that loan amount, credit rating, and loan purpose affect MSMEs on the Investree and Koinworks P2P lending platforms, while loan term, business duration, and financial statement information do not. These findings suggest that both P2P lending platforms consider various factors when evaluating MSME borrowers. The differences between Investree and Koinworks indicate distinct characteristics and appeals to MSMEs on each platform, which are also discussed in this study. The results of this research can assist MSMEs in preparing loan proposals to enhance credibility with lenders, provide better insights to lenders in decision-making, and serve as a reference for other P2P lending platforms in evaluating factors necessary for credit loan decision-making processes.

Pengaruh Perceived Ease of Use, Perceived Security, dan Perceived Reputation Terhadap Kepercayaan Awal Pemberi Pinjaman Pada Platform P2P Lending

This research objectives to examine the influence of perceived ease of use, perceived security, and perceived reputation on the initial trust of lenders on P2P lending platforms. The population under study consists of all Generation Z individuals residing in the city of Malang who have previously invested in P2P lending platforms. A sample of 180 respondents was selected through purposive sampling, with criteria including residency in Malang, aged between 17-26 years old, and having previous or current investment experience in P2P lending platforms. Data were collected through an online questionnaire and analyzed using Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach. The findings of the research reveal that perceived ease of use, perceived security, and perceived reputation significantly influence initial trust.

Improving credit risk assessment in P2P lending with explainable machine learning survival analysis

Recent research using explainable machine learning survival analysis demonstrated its ability to identify new risk factors in the medical field. In this study, we adapted this methodology to credit risk assessment. We used a comprehensive dataset from the Estonian P2P lending platform Bondora, consisting of over 350,000 loans and 112 features with a loan volume of 915 million euros. First, we applied classical (linear) and machine learning (extreme gradient-boosted) Cox models to estimate the risk of these loans and then risk-rated them using risk stratification. For each rating category we calculated default rates, rates of return, and plotted Kaplan–Meier curves. These performance criteria revealed that the boosted Cox model outperformed both the classical Cox model and the platform’s rating. For instance, the boosted model’s highest rating category had an annual excess return of 18% and a lower default rate compared to the platform’s best rating. Second, we explained the machine learning model’s output using Shapley Additive Explanations. This analysis revealed novel nonlinear relationships (e.g., higher risk for borrowers over age 55) and interaction effects (e.g., between age and housing situation) that provide promising avenues for future research. The machine-learning model also found feature contributions aligning with existing research, such as lower default risk associated with older borrowers, females, individuals with mortgages, or those with higher education. Overall, our results reveal that explainable machine learning survival analysis excels at risk rating, profit scoring, and risk factor analysis, facilitating more precise and transparent credit risk assessments.

Moral hazard in online peer-to-peer lending

ABSTRACT Does the shift towards reintermediation in the peer-to-peer (P2P) lending industry increase the market’s vulnerability to moral hazard by the platform? To address this question, we develop a model where the crowdfunding platform screens loan applications and assigns interest rates to listed loans for investors to fund. We find that when the platform’s only source of income is origination fees, the platform has the incentive to relax its screening effort. Relaxing the screening effort allows low-quality borrowers to list their loans on the platform, which is detrimental to lenders and sub-optimal from an aggregate welfare perspective. Our results imply that current regulations governing P2P lending platforms may not be sufficient to protect lenders from this moral hazard risk. We show that the platform’s moral hazard problem can be overcome when it has sufficient skin in the game.

Regulatory Framework of P2P Lending in India: Market Stability and Financial Inclusion Implications

Peer-to-peer (P2P) lending has revolutionized the financial industry by offering an alternative to conventional banking. It facilitates direct connections between lenders and borrowers via online platforms. This research paper examines the regulatory framework governing P2P lending in India, introduced by the Reserve Bank of India (RBI) in 2017, and its implications for market stability and financial inclusion. The study highlights the benefits of P2P lending, including greater acceptance, regional inclusivity, reduced costs, and faster loan approvals. It also explores the challenges posed by regulatory measures, such as caps on lending and borrowing, compliance costs, and operational hurdles, which can limit the impact on financial inclusion. The role of transparency in borrower information is analyzed, demonstrating how it influences loan interest rates, reduces adverse selection, and facilitates the formation of borrower groups based on credit ratings and social capital. Despite the significant benefits of enhanced transparency and regulatory oversight, the paper identifies ongoing challenges, including privacy concerns and information asymmetry. The findings underscore the need for continued regulatory refinement and collaboration among stakeholders to fully leverage the benefits of P2P lending in promoting financial inclusion and market stability in India. By addressing these challenges, P2P lending platforms can more effectively meet the credit needs of underserved populations, thereby contributing to a more inclusive financial system.

A computational model of the effects of borrower default on the stability of P2P lending platforms

Peer-to-peer (P2P) lending has attracted scholarly attention because of its economic significance and potential to democratize access to finance. However, P2P lending platforms face many challenges and failures that we need to understand more clearly. We build a computational model to study how borrower default affects P2P platform lending. We show that borrower default disrupts the P2P network formation process and undermines platform stability. Moreover, we find that defaults increase the inequality in accessing funding and provide a rationale for using curation rules, widely used in P2P platforms, in contrast to P2P insurance, which fosters cascading defaults. We also address a new trend in P2P lending platforms in which large companies (institutional investors) play an increasingly important role. We find that the presence of large companies creates a denser network (more loans) but generates a trade-off between making the platform more resilient to cascading defaults and more dependent on specific players. Overall, we explain how borrower defaults affect platform stability and what makes a platform vulnerable, threatening its survival. We discuss research and managerial insights into platform stability and the economic effect of P2P lending platforms.

Leveraging network topology for credit risk assessment in P2P lending: A comparative study under the lens of machine learning

Peer-to-Peer (P2P) lending markets have witnessed remarkable growth, revolutionizing the way borrowers and lenders interact. Despite the increasing popularity of P2P lending, it poses significant challenges related to credit risk assessment and default prediction with meaningful implications for financial stability. Traditional credit risk models have been widely employed in the field of P2P lending; however, they may not be capable to capture latent factor information inherent to a loan network based on similarity distances. Thus, in this study we propose an enhanced two-step modeling approach for Machine Learning (ML) that utilizes insights from network analysis and subsequently combines derived network centrality metrics with traditional credit risk factors to improve the prediction accuracy in the credit default prediction process. Through a comparative analysis of three classical ML models with varying degrees of complexity, namely Elastic Net (EN), Random Forest (RF), and Multi-Layer Perceptron (MLP), we showcase novel evidence that the systematic inclusion of network topology features in the credit scoring process can significantly improve the prediction accuracy of the scoring models. Additional robustness tests via the inclusion of randomly shuffled centrality metrics in the analysis, and a further comparison of the graph-based models against a pertinent state-of-the-art credit scoring model in form of XGBoost, further confirm our results. The insights from this study bear valuable conclusions for P2P lending platforms to further improve their scoring systems with graph-enhanced metrics, thereby reducing default risk and facilitating greater access to credit.

The Link between Family Financial Socialization in Adulthood and Investment Literacy of P2P Investors

AbstractThis paper examines how family financial socialization in adulthood is linked to the development of investment literacy among individual family members within the context of innovative financial services, specifically peer-to-peer (P2P) lending. Our findings revealed that P2P lending investors engage in a moderate level family financial socialization suggesting that family, as a key financial socialization agent in childhood and adolescence, maintains its role in adulthood. Additionally, such investors possess a high-level investment knowledge, skills, and attitudes. Explicit family financial socialization has a significant and positive effect on the individuals’ investment knowledge, skills, and attitudes, while the effect of implicit financial socialization is significant but negative for knowledge and attitudes. Such findings suggest that family discussion among adult members result in higher, while observations of family members’ investment behavior led to lower investment literacy. Our study found no significant moderating effect of the strength of social ties indicating that dynamics of family relations neither strengthen nor weaken proximal socialization outcomes. The analysis of differences across demographic groups unveiled statistically significant distinctions concerning respondents’ gender, income, and education. These results provide important insights for stakeholders, underscoring the significant role family socialization in adulthood plays in shaping individuals’ investment literacy, particularly of those investing on P2P lending platforms.

Machine Learning-Based Loan Default Prediction in Peer-to-Peer Lending

The peer-to-peer (P2P) lending market has recently undergone significant growth, transforming traditional lending practices. However, this evolution brings with it unique challenges, particularly in managing credit risk and ensuring the reliability of loan approvals. Accurate prediction of loan defaults remains a pivotal aspect of risk management in this sector. This study introduces a comprehensive approach to improve bad loan prediction in peer-to-peer (P2P) lending, sourcing Lending Club data. In the face of challenges posed by imbalanced datasets and risk management in the loan industry, our methodology significantly enhances prediction accuracy, particularly in identifying bad loans. The study implements a comprehensive process that includes data cleaning, feature engineering, feature selection, balancing the dataset and machine learning models, achieving a noteworthy accuracy rate over 92% and recall rate above 87%. This research advances academic understanding of loan prediction and generates real-world impact. The accurate models identifying explanatory provide a valuable framework for improving decision-making and strategic planning in P2P lending platforms.

Multi-view GCN for loan default risk prediction

As a significant application of machine learning in financial scenarios, loan default risk prediction aims to evaluate the client’s default probability. However, most existing deep learning solutions treat each application as an independent individual, neglecting the explicit connections among different application records. Besides, these attempts suffer from the problem of missing data and imbalanced distribution (i.e., the default records are small samples against all the applications). We believe similar records could provide some auxiliary signals, which are of critical importance to alleviate the data missing issue and facilitate data argumentation. To this end, we propose multi-view loan application graphs, dubbed MLAGs. By evaluating the similarity between the records, a loan application graph can be constructed. Furthermore, we arrange different similarity thresholds to organize various graph structures for multi-graph constructions; thus, a variety of representations can be generated via information propagation and aggregation for small sample argumentation. Consequently, the imbalanced data distribution and missing values issues can be alleviated effectively. We conduct experiments on three public datasets from real-world home credit and P2P lending platforms, which show that MGCN outperforms both conventional and deep learning models. Ablation studies also illustrated the validity of each module design.

The impact of fintech peer-to-peer lending and Islamic banks on bank performance during COVID-19

This study delves into the influence of Peer-to-Peer (P2P) Fintech lending on bank performance in Indonesia, with a specific focus on its effects on Islamic banks both before and during the COVID-19 pandemic. Employing a fixed-effects model, unbalanced panel data from 121 banks, including 16 Islamic banks, were analyzed. The findings unveil a significant and positive impact of growth loan disbursement to borrowers from P2P lending on bank performance, particularly in terms of return on assets. Additionally, Islamic Banks exhibit a significant and favorable effect on overall bank performance. Conversely, the joint interaction between P2P lending and Islamic Banks demonstrates a negative and significant influence on Islamic bank performance, suggesting that while P2P lending may benefit conventional banks, it adversely affects Islamic banks. Furthermore, this negative impact is exacerbated during the COVID-19 period. These outcomes underscore the importance of collaboration or strategic alliances between P2P lending platforms and Islamic banks, particularly in the context of the COVID-19 pandemic.

Public Perception of Online P2P Lending Applications

This study examines significant topics and customer sentiments conveyed in reviews of P2P lending applications (apps) in India by employing topic modeling and sentiment analysis. The apps considered are LenDenClub, Faircent, i2ifunding, India Money Mart, and Lendbox. Using Latent Dirichlet Allocation, we identified and labeled 11 topics: application, document, default, login, reject, service, CIBIL, OTP, returns, interface, and withdrawal. The sentiment analysis tool VADER revealed that most users have positive attitudes toward these apps. We also compared the five apps overall and on specific topics. Overall, LenDenClub had the highest proportion of positive reviews. We also compared the prediction abilities of six machine-learning models. Logistic Regression demonstrates high accuracy with all three feature extraction techniques: bag of words, term frequency-inverse document frequency, and hashing. The study assists borrowers and lenders in choosing the most appropriate application and supports P2P lending platforms in recognizing their strengths and weaknesses.

Fintech Based Investment Analysis (Peer to Peer Lending) in Sharia Economic Law and Positive Law

The development of digital technology today creates updates in all aspects of life, including investment. Investment is not something that is prohibited in Islam, but in practice, it must pay attention to Sharia principles. With the emergence of financial technology (Fintech)-based investment systems in various platforms, one of which is peer to peer (P2P) lending balanced with high levels of investment among millennials. In this case, Indonesian society, which is predominantly Muslim, must be careful in analyzing and making choices in investing, whether it is following the Shari’a and how the law regulates it. This research includes juridical-normative law research, to find out the review of Islamic law and positive law on fintech P2P lending and a conceptual approach to find out the concept of fintech P2P lending. The results of the study found that investment through the P2P lending platform is permissible in Islam with terms and conditions that are in accordance with Shari’ah principles. The contracts that can be used in P2P lending are al-bai’, ijarah, mudharabah, musyarakah, wakalah bi al ujrah, and qardh contracts. Every P2P Lending provider must obtain a permit from the Financial Services Authority (OJK) to run the entire lending system (POJK No. 77/POJK.01/2016) as well as follow the provisions of the DSN MUI Fatwa Number 117 of 2018. In positive law, it is regulated in the Civil Code, POJK Number 10 of 2022 and Bank Indonesia Regulation Number 19/12/PBI/2017.
 Keywords: investment, peer to peer lending, law

Peer-to-peer loan returns: heterogeneous effects across quantiles

ABSTRACT In this study, we examine how loan and borrowers’ characteristics have a different impact on profitable and non-performing loans. Using a quantile regression profit-scoring model estimated with 472 , 106 loans from the U.S. P2P lending platform Lending Club, we show that higher loan amounts, loan term, interest rate and lower income are associated with lower returns for less creditworthy borrowers, i.e. for under-performed loans. Conversely, for performing loans, higher loan amounts, loan term, interest rates and lower income are associated with higher returns. We also find that borrowers’ credit (debt-to-income and FICO score) matters mostly for the tails of the return distribution, to mitigate losses for non-performing loans and improve profits for highest-performing loans. The results have broader implications for the design of credit risk models.

Building Financial Capability of MSMEs through Financial Factor Analysis and its Effect on Lending Policy on P2P Lending Platforms

Purpose — This study aimed to analyze the effect of MSME financial factors, which consist of credit score, business turnover, leverage ratio, number of loans, company size, and profitability, on P2P lending policies. Design/methodology/approach—This study uses a quantitative research approach to test research hypotheses relevant to the research phenomenon. The logistic regression data analysis technique examined the factors that influence credit-granting decisions. Practical implications—This research's practical implications are that it will benefit MSME business actors by preparing important financial data to obtain alternative funding sources other than conventional financial services. Originality/value—This study reveals the financial variables that are important to consider in providing P2P loans to MSMEs, which is expected to provide hope for MSMEs to develop financial capabilities and increase business growth. Paper type — Case study

Operating peculiarities of P2P lending platforms in the Republic of Indonesia

Subject. This article discusses the operating peculiarities of P2P lending platforms in the Republic of Indonesia. Objectives. The article aims to analyze the key factors that contribute to the sustainable operation of P2P platforms, taking into account the structural, regulatory and operational aspects of their activities in Indonesia. Methods. For the study, I used the methods of analysis and generalization. Results. The study identifies the key drivers and factors that affect the stability of P2P platforms and outlines the direction for their further study. Conclusions and Relevance. The study highlights the importance of the impact of structural, regulatory, and operational aspects on the stability and efficiency of P2P platforms, pointing to the critical importance of properly organized regulation and governance. The findings can be used for legislative decisions of government agencies and be useful for researchers in studying trends in the development of digital platforms.

The Crystal Ball of User-Generated Content: Indication of P2P Lending Platform Failure

The Crystal Ball of User-Generated Content: Indication of P2P Lending Platform Failure