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

Credit Anomaly Detection Method based on Bayesian Networks

Due to the virtual nature of online transactions and the underdeveloped personal credit risk assessment system of Internet finance lending platforms in China, these platforms often struggle to verify the creditworthiness of applicants prone to fraud and default, leading to loan losses. As a result, risk control has become the core of Internet lending platforms. Modern risk control relies on statistical and data mining techniques to analyze and model data, uncover patterns that indicate loan defaults, and identify anomalies. This paper proposes a Bayesian network-based credit anomaly detection method, which detects anomalies by calculating and ranking the joint anomaly probabilities of sample instances. The technique operates under unsupervised learning and can effectively handle missing and imbalanced data. This method analyzes a loan credit dataset with 32,581 samples and 12 features of the customers. The result of the analysis demonstrates the feasibility and effectiveness of this method in detecting anomalies in online loans.

Mitigating Financial Fraud in Indonesia: A Grounded Theory Approach to Fraud Detection in P2P Lending, Rural Banks, and Conventional Banks

The rapid digitalization of Indonesia’s financial sector, driven by the growth of Peer-to-Peer Lending (P2PL) platforms, Rural Credit Banks (BPR), and conventional banks, has expanded financial inclusion but also heightened the risk of fraud. This study investigates the fraud detection systems (FDS) employed by these diverse financial institutions using a grounded theory approach. By conducting semi-structured interviews with key stakeholders—including directors, compliance managers, and fraud control officers—the research identifies critical factors influencing fraud detection in Indonesia’s financial ecosystem. The study reveals that while larger institutions leverage advanced machine learning models and real-time transaction monitoring, smaller entities like BPRs rely heavily on manual oversight due to resource constraints. Furthermore, the integration of multi-subject perception differences is highlighted as a novel and effective approach for fraud detection, particularly in P2PL platforms, by analyzing multiple data sources to reduce bias and increase accuracy. However, the study also uncovers significant challenges in implementing robust FDS, including high false-positive rates, regulatory complexities, and resource limitations, particularly in smaller institutions. The findings emphasize the need for a tailored fraud detection framework that addresses the unique needs of each sector, balancing technological advancements with operational feasibility.

Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence

The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are more accessible than ever, but it has been challenging to create and implement systems that support real-world financial applications, primarily due to their lack of transparency and explainability—both of which are essential for building trustworthy technology. The novelty of this study lies in the development of an explainable AI (XAI) model that not only addresses these transparency concerns but also serves as a tool for policy development in credit risk management. By offering a clear understanding of the underlying factors influencing AI predictions, the proposed model can assist regulators and financial institutions in shaping data-driven policies, ensuring fairness, and enhancing trust. This study proposes an explainable AI model for credit risk management, specifically aimed at quantifying the risks associated with credit borrowing through peer-to-peer lending platforms. The model leverages Shapley values to generate AI predictions based on key explanatory variables. The decision tree and random forest models achieved the highest accuracy levels of 0.89 and 0.93, respectively. The model’s performance was further tested using a larger dataset, where it maintained stable accuracy levels, with the decision tree and random forest models reaching accuracies of 0.90 and 0.93, respectively. To ensure reliable explainable AI (XAI) modeling, these models were chosen due to the binary classification nature of the problem. LIME and SHAP were employed to present the XAI models as both local and global surrogates.

The Application of Fintech (Financial Technology) and Risk Management

This paper provides a comprehensive overview of the impact and applications of financial technology (Fintech) across various sectors such as payment and clearing, lending, wealth management, insurance, and risk management. It highlights the transformative potential of Fintech in enhancing financial efficiency, inclusiveness, and risk management through the utilization of modern technologies like big data, artificial intelligence, and blockchain. The paper delves into specific examples such as online payment systems, peer-to-peer lending platforms, robo-advisory services, and blockchain technology. Furthermore, it discusses the challenges and risks associated with Fintech, including regulatory issues, security concerns, and the need for effective risk management practices. The abstract emphasizes the importance of responsible risk management in ensuring financial stability and preventing systemic risks in the Fintech sector. Overall, the abstract provides a comprehensive understanding of the role of Fintech in shaping the future of the financial industry and emphasizes the need for effective regulation, risk management, and technological innovation to harness its full potential while mitigating associated risks.

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.

Role transition, investment practice and risk Management of Peer-to-peer Lending Investors: Based on the perspective of investor learning

Online peer-to-peer (P2P) lending has emerged as a result of the integration of finance and technology. Within the P2P lending market, individual investors have the opportunity to make repeated investments and engage in two-way transactions involving loans and investments. This setting provides an ideal scenario for studying the impact of different learning types on investors' risk management. Drawing on transaction data from the online lending platform Renrendai.com, we empirically examine the influence of role transition and investment practice on investors' risk identification, risk preference, and investment performance. We introduce variables related to loans and investment portfolios to analyze the effects. Our findings reveal that investment practice has a greater effect than role transition in reducing the investment default of online lending investors. Additionally, role transition is associated with a decrease in the investment amount set by investors and a reduced reliance on borrowers' credit ratings. Historical investment experience also leads to a decrease in the investment amount, while experiences of past investment failures improve investors' reference to borrowers' credit ratings. Furthermore, we observe that borrowing experience and previous investment failures contribute to a lower probability and proportion of high-risk loans in the portfolio. We shed light on the “black box” of investor learning and expand the research methodology for understanding investor behavior characteristics in the Fintech era.

When young muslims meets online lending: Examining legal awareness and assessment of lending platforms

This study aims to fill the gap in the literature by investigating the perspectives of young Muslims on online lending platforms. The article explores young Muslims' understanding of online lending based on Sharia principles and their perceptions of the ethical and legal dilemmas associated with online lending, which are closely linked to their religious and social knowledge. This study starts from the assumption that online lending involves various issues intricately connected to recent legal and financial advancements. Online lending encompasses not only economic and financial matters but also ethical, moral, and legal issues. The research findings indicate that the understanding of Muslim youth regarding the legal aspects of online lending in the study area is quite high. Data shows that 62% of the Muslim youth (student) respondents are knowledgeable about both Sharia compliance and national legal issues concerning online lending. However, the data also reveals that only approximately 60% of the respondents know how to handle legal issues involving online lending platforms. This implies that respondents need further education and information on these matters. Following data collection, we provided socialization and information on legal awareness and the evaluation of online lending platforms, which improved their understanding. The findings of this study highlight several potential directions for future research, such as assessing the effectiveness of legal awareness programs for digital financial services and P2P lending, and creating more effective methods for legal education. Additionally, future studies should expand to include different cities and regions and a broader range of respondents to achieve a deeper understanding of online lending and legal awareness.

Impact of behavioral factors on the investor’s intention in peer-to-peer lending

PurposePeer-to-peer lending has emerged as a promising alternative investment avenue globally. This study explores the variables influencing investors' intention in peer-to-peer lending. Studying these factors is crucial for inspiring investor motivation, which leads to the growth of peer-to-peer lending platforms in India.Design/methodology/approachThe research utilizes information gathered from 293 investors of the Rang de platform through a questionnaire. It employs ordinal logistic regression to assess how demographic characteristics, investor experience, social projects and investment factors influence the study’s outcomes.FindingsThe research indicates that the education qualification and income level of the investors are significant demographic variables in their investment decisions. Moreover, social projects, the experience of the investors and investment factors affect their intent to invest more in peer-to-peer lending projects.Research limitations/implicationsIt helps stakeholders and borrowers of crowdfunding platforms think about these aspects when creating plans to bring in and keep investors. This approach also enhances the openness and dependability of lending processes.Originality/valueThis research sheds light on Indian investors' behavior toward peer-to-peer lending platforms and includes new investment factors like project categories and impact partners that other researchers did not identify.

The Impact of U.S. Regulatory Frameworks on Asset Quality in Peer-To-Peer Lending Platforms

The Impact of U.S. Regulatory Frameworks on Asset Quality in Peer-To-Peer Lending Platforms

Financial Management and Internal Controls in the Context of Asset Quality: Lessons from U.S. Peer-To-Peer Lending Platforms

Financial Management and Internal Controls in the Context of Asset Quality: Lessons from U.S. Peer-To-Peer Lending Platforms

Beyond banks: Navigating the shift to peer-to-peer lending for small enterprises

This study sheds light on how non-traditional lending avenues, specifically peer-to-peer (P2P) lending platforms, influence the financing decisions of small businesses. It introduces a theoretical framework where borrowers weigh the option between opting for a cost-effective traditional bank loan versus an expensive option through crowdlending platforms. The findings suggest that crowdlending platforms become a more appealing choice in the event of credit supply disruptions in traditional banking sectors. Leveraging the phased introduction of mobility restrictions during the COVID-19 pandemic as a case study, our research demonstrates a noticeable pivot of small businesses towards alternative funding sources, such as P2P lending. These findings emphasize the value of offering a variety of financial tools to small businesses so they can weather economic storms.

Social Media Sentiment and Gender on Peer-To-Peer Lending Performance: Machine Learning Approaches

One innovation brought about by fintech is peer-to-peer lending. However, in recent years with rapid growth, unfortunately, there are still many negative sentiments about the peer-to-peer lending industry. Especially with the advancement of technology and the internet, all information about peer-to-peer lending is easily accessible through social media. The amount of negative information about peer-to-peer lending can influence opinions or sentiments towards the performance of peer-to-peer lending platforms and invite the public to comment on social media. Additionally, the gender factor of the information provider also affects the performance of the peer-to-peer market. Based on the explanation above, there has never been any previous research conducted in Indonesia, so this study will examine how the influence of social media information sentiment and the gender factor of the information provider affects peer-to-peer lending performance. The analysis method used is logistic regression, and the data used is obtained through machine learning using the Python programming language on Google Collab from social media opinions and the gender that provides opinions on social media. Meanwhile, the financial performance of peer-to-peer lending is taken from the annual performance report of the Financial Services Authority (OJK) by default. From the results of this study, it was found that the gender of the information provider affects the financial performance of peer-to-peer lending. However, simultaneously, social media sentiment and the gender of the information provider affect the financial performance of peer-to-peer lending.

The Role of Platforms in Fostering Sustainable Finance: A Comprehensive Approach

As the global financial ecosystem undergoes a paradigm shift toward sustainability, platforms emerge as instrumental intermediaries, connecting diverse stakeholders, facilitating information flow, and catalyzing impactful investments. This paper analyses the evolving landscape of sustainable finance and investigates the role of platforms in fostering its growth. Sustainable finance platform-based enablers were determined using a systematic literature review and bibliometric techniques on a sample of papers retrieved from the SCOPUS database, and included crowdfunding platforms, impact investment platforms, peer-to-peer (P2P) lending platforms, blockchain-based financing platforms, and ESG data platforms. The analysis showed that platform-based solutions act as accelerators of sustainable finance mobilization, by enhancing transparency of the processes, and by improving dissemination and accessibility of the funds needed. Thus, platform-based solutions help a broader set of stakeholders direct the potential of platforms to accelerate the transition toward a more sustainable and inclusive global financial system.

Obstacles in the Implementation of Online Loans in Indonesia (Based on Protection Law Consumer)

Online loans are platforms that allow individuals or businesses to apply for and obtain loans over the internet. Online loans offer convenience and accessibility. However, there are also illegal online loans that are harmful to their users. This is the background for the emergence of two problem formulations, namely how legal regulations governing the practice of online loans and how consumer legal protection in online loans. The research method used is normative juridical. The results found that Financial Services Authority (Otoritas Jasa Keuangan/OJK) as the responsible authority has issued Financial Services Authority Regulations (Peraturan Otoritas Jasa Keuangan/POJK) No. 77/POJK.01/2016 on Information Technology-Based Money Lending and Borrowing Services. However, the implementation and effectiveness of this regulation is still a big challenge. Many online lending platforms operate without a license and ignore the applicable regulations, thus posing a high risk to consumers. Recipients of online loans can be said to be consumers because they are users of a service. In this case, online loan services, consumer protection has been regulated in Law No. 8 of 1999 of Consumer Protection, and regarding consumer protection in online loans has also been regulated in POJK No. 77/POJK.01/2016 and Bank Indonesia Regulation No. 3 of 2023 on Bank Indonesia Consumer Protection, although there are already regulations governing online loan consumers, online loan consumer protection is still a problem because many illegal online loans are always looking for loopholes and circumvent existing regulations.

Sample selection bias in non-traditional lending: A copula-based approach for imbalanced data

Credit scoring models for non-traditional lending channels, such as peer-to-peer (P2P) lending platforms, are usually estimated only on the sample of accepted applicants. This may lead to biased estimates of the risk drivers. This issue can be addressed using a reject inference technique that includes the characteristics of rejected applicants in the model. Due to the low numbers of accepted applicants and default records, credit scoring models usually face a class imbalance problem. However, previous literature on sample selection models for credit scoring does not address the class imbalance issue. To fill this gap, we extend the Generalised Extreme Value (GEV) regression model for binary data to the sample selection framework. We consider the quantile function of the GEV distribution as a link function in both the selection and outcome equations. We use the copula function to model the dependence structure between the two equations for its flexibility. This proposal is called the Sample Selection Generalised Extreme Value (SSGEV) model and it is implemented in the R package BivGEV. We apply this model to a comprehensive dataset provided by Lending Club, and we show that parameter estimates obtained only on accepted P2P applicants are biased and coherently with the literature. The SSGEV model achieves a higher predictive accuracy than those obtained using univariate approaches or a sample selection probit model. Our proposal also provides more conservative estimates of the Value-at-Risk and the Expected Shortfall.

Striking a Balance: Evaluating Credit Risk with Traditional and Machine Learning Models

This research assesses machine learning models' validity, clarity, and equity, compared to classical models and especially logistic regression in credit risk evaluation. In the traditional model of data management, efficiency and the accuracy of information are challenges; an issue of machine learning models is model selection and multicollinearity. The study intends to help financial institutions establish the best strategy for their needs. Furthermore, it delves into the effect of heterogeneous data sources on the credit risk model using machine learning. The research analyses the implications of using machine learning in assessing credit risk. Interestingly, focusing on peer-to-peer lending platforms, the research aims to deal with the need for more attention to combining machine learning and traditional models in the literature. The deductive method is the application of inferential analyses, the Traditional model is logistic regression, and the Machine Learning model is a neural network (CNN model) based on secondary data from the Kaggle peer-to-peer lending dataset. With likely findings expected to comprise prediction of the probability of default and better availability of loans, risk analysis leads to formulated lending decisions managing a financial portfolio.

Automatic versus manual investing: Role of past performance

Using unique data from a leading peer-to-peer (P2P) lending platform, we investigate the link between past investment performance and choice of auto-investing tool. Our results suggest that investors who experience fewer defaults in the manual mode are more inclined to switch to automatic investment. Several factors account for this relationship, including investor inattention, decision speed, investment delegation, and experience. Regarding the latter, our results suggest that experienced investors are more likely to continue self-directed bidding, even if they have faced defaults in manual investments in the past. These investors may attribute their previous mistakes to their own actions rather than the limitations of the self-directed bids. Our results are robust to alternative specifications.

Blockchain and sustainable finance: Enhancing transparency and efficiency in green investments

The study aimed to analyse the possibilities of using blockchain technology to increase transparency and efficiency in sustainable finance. The application of blockchain technologies and environmental and social practices in the context of sustainable finance was analysed, emphasising implementation and prospects in Kyrgyzstan. Blockchain, as an innovative decentralised data recording technology, provides a high level of security, transparency and immutability, which renders it particularly valuable for the financial sector. The study highlighted how blockchain can improve international transfer processes, increase the transparency of financial transactions and simplify the management of smart contracts, and noted existing challenges such as scalability and legal issues. The role of blockchain technologies in sustainable finance, especially in the field of green investments, was highlighted as an important aspect of the study. The introduction of blockchain in areas such as green project financing and social initiative management can help increase investor confidence and improve investment performance. Financial technology is also central in shaping new approaches to finance and investment, facilitating access to capital through crowdfunding and person-to-person (P2P) lending platforms. The introduction of environmental and social practices in financial institutions of the Kyrgyz Republic demonstrates the growing attention to corporate responsibility and sustainable development, despite the initial stage of their implementation. On international stage, successful projects such as the use of blockchain to track supply chains, green bonds and carbon credit management demonstrate the potential of technology to improve the transparency and efficiency of sustainable finance. These examples can be used by Kyrgyzstan in developing and implementing domestic blockchain and environmental and social initiatives, contributing to more sustainable economic growth and attracting international investment

ANALISIS DALAM BERINTERAKSI PADA PEER TO PEER LENDING DALAM KREDIT MACET PINJAMAN ONLINE DAN PAYLATER YANG DIDOMINASI OLEH GENERASI Z

This research analyzes interactions on peer to peer lending platforms with a focus on bad credit online loans and paylater dominated by generation Z. Peer to peer lending has become an easy financing alternative in Indonesia that can be done digitally. However, the rapid growth of peer to peer lending also poses risks, including significant bad debts. This study aims to identify the development of peer to peer lending in Indonesia from 2020 to 2023 and examine the mitigation of bad credit risk in online loans and paylater that are widely used by generation Z. The research method used is qualitative with a descriptive method using a literature review approach, analyzing secondary data from various reliable sources such as OJK reports and journal articles. The research findings show that although peer to peer lending provides easy access to financing, many generation Zs are trapped in bad credit due to their inability to repay loans on time. This research provides recommendations for borrowers and lenders to be more careful in using peer to peer lending services that are legal and supervised by OJK to reduce the risk of bad credit.