Deep Learning Model Implementation Using Convolutional Neural Network Algorithm for Default P2P Lending Prediction

Peer-to-peer (P2P) lending is one of the innovations in the field of fintech that offers microloan services through online channels without intermediaries. P2P  lending facilitates the lending and borrowing process between borrowers and lenders, but on the other hand, there is a threat that can harm lenders, namely default.  Defaults on  P2P  lending platforms result in significant losses for lenders and pose a threat to the overall efficiency of the peer-to-peer lending system. So it is essential to have an understanding of such risk management methods. However, designing feature extractors with very complicated information about borrowers and loan products takes a lot of work. In this study, we present a deep convolutional neural network (CNN) architecture for predicting default in P2P lending, with the goal of extracting features automatically and improving performance. CNN is a deep learning technique for classifying complex information that automatically extracts discriminative features from input data using convolutional operations. The dataset used is the Lending Club dataset from P2P lending platforms in America containing 9,578 data. The results of the model performance evaluation got an accuracy of 85.43%. This study shows reasonably decent results in predicting p2p lending based on CNN. This research is expected to contribute to the development of new methods of deep learning that are more complex and effective in predicting risks on P2P lending platforms

Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

With the accelerated pace of market-oriented reform, Internet finance has gained a broad and healthy development environment. Existing studies lack consideration of time trends in financial risk, and treating all features equally may lead to inaccurate predictions. To address the above problems, we propose an LSTM model based on sliding window and attention mechanism. The model uses sliding windows to enable the model to effectively exploit the contextual relevance of loan data. And we introduce the attention mechanism into the model, which enables the model to focus on important information. The result on the Lending Club public desensitization dataset shows that our model outperforms ARIMA, SVM, ANN, LSTM, and GRU models

Deep Learning Model Implementation Using Convolutional Neural Network Algorithm for Default P2P Lending Prediction

Peer-to-peer (P2P) lending is one of the innovations in the field of fintech that offers microloan services through online channels without intermediaries. P2P lending facilitates the lending and borrowing process between borrowers and lenders, but on the other hand, there is a threat that can harm lenders, namely default. Defaults on P2P lending platforms result in significant losses for lenders and pose a threat to the overall efficiency of the peer-to-peer lending system. So, it is essential to have an understanding of such risk management methods. However, designing feature extractors with very complicated information about borrowers and loan products takes a lot of work. In this study, we present a deep convolutional neural network (CNN) architecture for predicting default in P2P lending, with the goal of extracting features automatically and improving performance. CNN is a deep learning technique for classifying complex information that automatically extracts discriminative features from input data using convolutional operations. The dataset used is the Lending Club dataset from P2P lending platforms in America containing 9,578 data. The results of the model performance evaluation got an accuracy of 85.43%. This study shows reasonably decent results in predicting p2p lending based on CNN. This research is expected to contribute to the development of new methods of deep learning that are more complex and effective in predicting risks on P2P lending platforms

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

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

Managing credit risk and the cost of equity with machine learning techniques

Credit risks and the cost of equity can influence market participants' activities in many ways. Providing in-depth analysis can help participants reduce potential costs and make profitable strategies. This kind of study is usually armed with conventional statistical models built with researchers' knowledge. However, with the advancement of technology, a massive amount of financial data increasing in volume, subjectivity, and heterogeneity becomes challenging to process conventionally. Machine learning (ML) techniques have been utilised to handle this difficulty in real-life applications. This PhD thesis consists of three major empirical essays. We employ state-of-art machine learning techniques to predict peer-to-peer (P2P) lending default risk, P2P lending decisions, and Environmental, Social, Corporate Governance (ESG) effects on firms' cost of equity. In the era of financial technology, P2P lending has gained considerable attention among academics and market participants. In the first essay (Chapter 2), we investigate the determinants of P2P lending default prediction in relation to borrowers' characteristics and credit history. Applying machine learning techniques, we document substantial predictive ability compared with the benchmark logit model. Further, we find that the LightGBM has superior predictive power and outperforms all other models in all out-of-sample predictions. Finally, we offer insights into different levels of uncertainty in P2P loan groups and the value of machine learning in credit risk mitigation of P2P loan providers. Macroeconomic impact on funding decisions or lending standards reflects the risk-taking behaviour of market participants. It has been widely discussed by academics. But in the era of financial technology, it leaves a gap in the evidence of lending standards change in a FinTech nonbank financial organisation. The second essay (Chapter 3) aims to fill the gap by introducing loan-level and macroeconomic variables into the predictive models to estimate the P2P loan funding decision. Over 12 million empirical instances are under study while big data techniques, including text mining and five state-of-the-art approaches, are utilised. We note that macroeconomic condition affects individual risk-taking and reaching-for-yield behaviour. Finally, we offer insight into macroeconomic impact in terms of different levels of uncertainty in different P2P loan application groups. In the third essay (Chapter 4), we use up-to-date machine learning techniques to provide new evidence for the impact of ESG on the cost of equity. Using 15,229 firm-year observations from 51 different countries over the past 18 years, we document negative causal effects on the cost of equity. In addition, we uncover non-linear effects because the level of ESG effects on the equity cost decrease with the enhancements of ESG performance. Furthermore, we note the heterogeneity in ESG effects in different regions by breaking down our sample. Finally, we find that global crises change the sensitivity of the equity cost towards ESG, and the change varies in areas

Legal Protection Against Peer-to-Peer Lending-Based Financial Technology Losses: An Analysis of Islamic Law Contracts and Positive Law

This article discusses the analysis of Islamic legal contracts and positive laws concerning the legal protection against financial technology losses based on peer-to-peer lending. This discussion is based on two raised subtopics namely 1) The importance of legal protection against the losses of the parties involved in this transaction and 2) The analysis regarding the contract used in this transaction, which is based on two perspectives, the Islamic legal contracts and positive law. Furthermore, this qualitative research uses a comparative approach. The data analyzed in this study were both primary and secondary. The primary and secondary data were collected from interviews conducted via different platforms with various related sources and from materials, such as journals, articles, and theses, respectively. The results of this study show the importance of legal protection for every party involved in peer-to-peer lending-based financial technology transactions. Some legal issues that have  been frequently experienced, include defaults, misuse of personal data, and even the threat of terror received by debtors. Additionally, reviews regarding the contract in the transaction imposed by the organizer were based on 2 perspectives, namely Islamic law and positive law. The transaction process will only become illegal when it conflicts the Islamic laws with the elements of usury and interest in it. And based on the perspective of positive law itself, if there is a defect or anything contrary to decency or public order, the agreement or contract becomes null and void.This article discusses the analysis of Islamic legal contracts and positive laws concerning the legal protection against financial technology losses based on peer-to-peer lending. This discussion is based on two raised subtopics namely 1) The importance of legal protection against the losses of the parties involved in this transaction and 2) The analysis regarding the contract used in this transaction, which is based on two perspectives, the Islamic legal contracts and positive law. Furthermore, this qualitative research uses a comparative approach. The data analyzed in this study were both primary and secondary. The primary and secondary data were collected from interviews conducted via different platforms with various related sources and from materials, such as journals, articles, and theses, respectively. The results of this study show the importance of legal protection for every party involved in peer-to-peer lending-based financial technology transactions. Some legal issues that have  been frequently experienced, include defaults, misuse of personal data, and even the threat of terror received by debtors. Additionally, reviews regarding the contract in the transaction imposed by the organizer were based on 2 perspectives, namely Islamic law and positive law. The transaction process will only become illegal when it conflicts the Islamic laws with the elements of usury and interest in it. And based on the perspective of positive law itself, if there is a defect or anything contrary to decency or public order, the agreement or contract becomes null and void

Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets

For the emerging peer-to-peer (P2P) lending markets to survive, they need to employ credit-risk management practices such that an investor base is profitable in the long run. Traditionally, credit-risk management relies on credit scoring that predicts loans’ probability of default. In this paper, we use a profit scoring approach that is based on modeling the annualized adjusted internal rate of returns of loans. To validate our profit scoring models with traditional credit scoring models, we use data from a European P2P lending market, Bondora, and also a random sample of loans from the Lending Club P2P lending market. We compare the out-of-sample accuracy and profitability of the credit and profit scoring models within several classes of statistical and machine learning models including the following: logistic and linear regression, lasso, ridge, elastic net, random forest, and neural networks. We found that our approach outperforms standard credit scoring models for Lending Club and Bondora loans. More specifically, as opposed to credit scoring models, returns across all loans are 24.0% (Bondora) and 15.5% (Lending Club) higher, whereas accuracy is 6.7% (Bondora) and 3.1% (Lending Club) higher for the proposed profit scoring models. Moreover, our results are not driven by manual selection as profit scoring models suggest investing in more loans. Finally, even if we consider data sampling bias, we found that the set of superior models consists almost exclusively of profit scoring models. Thus, our results contribute to the literature by suggesting a paradigm shift in modeling credit-risk in the P2P market to prefer profit as opposed to credit-risk scoring models