Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6, 5336-5348 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i6.3190 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: kusumawatirita@umy.ac.id Peer-to-peer lending: An analytical review of research trends and future prospects Rita Kusumawati1*, Mamduh M Hanafi2, Arni Surwanti3, Alni Rahmawati4 1,3,4Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia; kusumawatirita@umy.ac.id (R.K.) 2Universitas Gadjah Mada, Yogyakarta, Indonesia. Abstract: This study aims to identify peer-to-peer lending research trends in the Scopus database from 2017 to 2023. This research uses qualitative methods and a literature study approach. The data source in this study was taken from the Scopus database with the keyword "peer AND to AND peer AND lending" with the publication year of 2017-2023. After applying a set of criteria, 202 articles were used in subsequent analyses. All selected articles are manually read and coded to get some identifiable information from each article. The information includes country setting, journal quality, research method, theories applied in research, and research findings. The reported results are discussed based on theme clusters using bibliometric analysis to classify articles based on the similarity of the authors' keywords. The prominence of peer-to-peer Lending as a study issue is evident from the high caliber of publications predominantly published in esteemed journals categorized as Q1, Q2 and Q3. Conversely, articles specifically focused on peer-to-peer Lending published in Q4 are scarce. The article's keyword grouping identified six cluster themes: Financial sector dynamics, Algorithm decision framework, connected business infotech, P2P Market Crowdfunding Hub, and financial innovation. This study is the first to investigate peer-to-peer lending research trends using literature review and bibliometric analysis. The study focused on articles published between 2017 and 2023. Keywords: Bibliometric, Peer-to-peer lending, Systematic literature review. 1. Introduction Peer-to-peer (P2P) lending has become one of the leading innovations in the modern financial industry. In recent years, P2P Lending has gained significant popularity in various countries. P2P lending is a developing internet lending sector that operates outside of the existing traditional financial system(Liu et al., 2020a). Peer-to-peer (P2P) lending platforms are online marketplaces that act as traditional intermediaries and free mediators between lenders and borrowers (S. Chen et al., 2020). Peer- to-peer Lending provides an online platform that connects borrowers and lenders(Hsu, Li, and Bao, 2021), conducted online through various lending platforms and self-developed credit check tools for P2P Lending companies(C. Wang et al., 2019). P2P lending's primary function is connecting lenders and borrowers for loans with a set interest rate, principal amount, and due date (Yu & Shen, 2019). The principle of peer-to-peer (P2P) lending is that peers (individuals, businesses, groups, etc.) borrow from one another directly without the need for a middleman, discussing and agreeing upon the lending terms (Caplescu et al., 2020). The world's first online P2P lending platform started in 2005 in the UK with the establishment of the Zopa platform (Q. Yang &; Lee, 2016); (Klein et al., 2021; X. Yang, 2014). In the early 2010s, P2P lending began to flourish in the United States with the advent of platforms such as LendingClub and Prosper. P2P lending has seen rapid growth in the US, attributed to regulatory support from the US government and advancements in technology, as well as the ease of internet access (S. Chen et al., 2020). This growth trend has not been confined to the United States and Europe, as it has spread to Asian countries such as China, India, and Indonesia. P2P lending entered China around 2006 (Yu & Shen, 5337 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5336-5348, 2024 DOI: 10.55214/25768484.v8i6.3190 © 2024 by the authors; licensee Learning Gate 2019), and it has rapidly developed since 2012 (Y. Li et al., 2020), characterized by the establishment of numerous new platforms and an increasing number of lenders and borrowers. Among these platforms, the fastest-growing P2P lending platform in China is Paipaidai.com (C. Chen et al., 2021; Y. Li et al., 2020). Indonesia has also witnessed significant growth in P2P lending (Santoso et al., 2020). As of June 2023, data from the Indonesia Financial Services Authority (OJK) indicates that there are 102 registered P2P lending platforms in Indonesia, with total assets amounting to IDR 6,825.39 billion. These platforms serve a substantial number of borrowers, with approximately 115,801,742 entity units having borrower accounts, along with 1,087,654 entity units holding lender accounts (ojk.go.id, 2023). P2P lending offers several advantages, such as more accessible access to financing and competitive interest rates. P2P lending can help small enterprises and individual consumers with short-term financing (Y. Li et al., 2020). The presence of P2P Lending is a solution to meet people's needs for financial services (Suryono et al., 2021). Peer-to-peer (P2P) online lending platforms provide alternative credit options for individuals and small businesses (A. Basha et al., 2021). P2P lending allows borrowers to obtain microloans from individuals without the intermediation of financial institutions (Gibilaro & Mattarocci, 2018). In addition to the advantages offered by P2P lending, various challenges need to be overcome. For example, the risk of loan default, information imbalance between borrowers and lenders, and regulatory and consumer protection issues. In this context, it is crucial to conduct a comprehensive literature review of research done previously in the field of P2P lending. This literature review will help understand trends, key issues, and the latest developments in P2P Lending. It will also provide a solid foundation for further research and help identify knowledge gaps that need to be filled. By looking at an in-depth literature review of P2P lending, we can better understand this phenomenon, estimate potential future developments, and identify areas of research that are still not well covered. This study intends to assess the breadth and significance of P2P lending by responding to the following research questions, considering the importance and implications of all these concerns for P2P lending. RQ1: What has been done by the existing research on P2P lending (country setting, and research method)? RQ2: What authors, scientific articles, and theories applied in the research are most influential? RQ3: What themes and findings are found in existing research on P2P lending? Based on the background explanation above, this paper will analyze P2P Lending. As for the novelty in this study, the author mapped a survey of P2P Lending that had been published previously using the Scopus database. Then, from this mapping, this research will find new gaps to be used as follow-up research and can complement the existing literature. We organize this research as follows. Section 2 presents the research method. Section 3 provided the evolution of the literature, publication quality, research setting, research method, theories applied in the research, and most cited articles. To determine the study theme covered in this literature review, this part also includes a bibliometric analysis, and Section 4 outlines the conclusion and limitations. 2. Method This research uses qualitative methods and a literature study approach. The data source in this study was taken from the Scopus database with the keyword "peer AND to AND peer AND lending" with the publication year of 2017-2023. All journal articles belonging to the economy and business categories were included in this study. The articles in this study may have been downloaded due to accessibility requirements. In addition, the articles are written in English. The quality of papers is evaluated using Scimago's journal rankings, with non-indexed journals excluded. In addition, articles that do not directly relate to the research subject are also eliminated. Of the initial 402 articles, 202 remain after all criteria have been applied. For subsequent analysis, the data were recorded in a research information system (RIS) and comma-separated values format (CSV), together with the required citations and bibliographic data. 5338 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5336-5348, 2024 DOI: 10.55214/25768484.v8i6.3190 © 2024 by the authors; licensee Learning Gate Table 1. Methodology for searching references. Search terms “Peer-to-peer lending” Search filed Article title, abstract, keywords Query string ( peer AND to AND peer AND Lending ) AND ( LIMIT-TO ( SUBJAREA , "ECON" ) OR LIMIT-TO ( SUBJAREA , "BUSI" ) ) AND ( LIMIT-TO ( DOCTYPE , "ar" ) ) AND ( LIMIT-TO ( LANGUAGE , "English" ) ) AND ( LIMIT-TO ( EXACTKEYWORD , "Peer-to-peer Lending" ) OR LIMIT- TO ( EXACTKEYWORD , "P2P Lending" ) ) AND ( LIMIT-TO ( SRCTYPE , "j" ) ) AND ( LIMIT-TO ( PUBSTAGE , "final" ) ) Period time 2017-2023 Document type Journal article Language English Keyword P2P lending, peer-to-peer lending All selected articles are manually read and coded to get some identifiable information from each article. The information includes country setting, journal quality, research method, theories applied in research, and research findings. The reported results are discussed based on theme clusters using bibliometric analysis to classify articles based on the similarity of the authors' keywords. Bibliometric is a potent instrument for finding and managing information, and it offers insightful analytical outcomes across a range of disciplines(Liu et al., 2020a). This bibliometric analysis was performed using VOSviewer software. This tool provides mapping-based visualizations of bibliometric networks, where the distance between any two nodes indicates how closely they are related (Sustach et al., 2022). 3. Results and Discussion 3.1. Evolution of the Literature Figure 1 depicts the annual frequency of publications spanning seven years from 2017 to 2023. During the initial three years (2017-2019), the number of articles addressing the topic of P2P lending remained relatively low, with 13 articles in 2017, 19 articles in 2018, and 21 articles in 2019. However, a notable surge occurred in 2020, with the publication count reaching 42 articles, followed by a slight increase to 43 articles in 2021. Subsequently, in 2022, there was a decline to 29 articles, which then saw a marginal rise to 35 articles in 2023. Figure 1. Frequency distribution of publication article by year. 5339 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5336-5348, 2024 DOI: 10.55214/25768484.v8i6.3190 © 2024 by the authors; licensee Learning Gate 3.2. Top 10 Most Cited Articles Table 5 shows the top 10 citations in P2P research out of 202 articles reviewed. The most cited article is “Do fintech lenders penetrate areas that are underserved by traditional banks?” Table 2. Top 10 most cited articles. No. Author Title Year Total citation (N) Journal SJR rank 1 (Jagtiani & Lemieux, 2018) Do fintech lenders penetrate areas that are underserved by traditional banks? 2018 188 Journal of Economics and Business Q2 2 (Wei & Lin, 2017) Market mechanisms in online peer-to-peer lending 2017 166 Management Science Q1 3 (Ge et al., 2017) Predicting and Deterring Default with Social Media Information in Peer-to-Peer Lending 2017 148 Journal of Management Information Systems Q1 4 (Xia et al., 2017) Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending 2017 138 Electronic Commerce Research and Applications Q1 5 (Freedman & Jin, 2017) The information value of online social networks: Lessons from peer-to-peer lending 2017 137 International Journal of Industrial Organization Q1 6 (Jagtiani & Lemieux, 2019b) The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform 2019 114 Financial Management Q1 7 (Lin et al., 2017) Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China 2017 102 Applied Economics Q2 8 (Liu et al., 2020b) What have we learnt from 10 years of fintech research? a scientometric analysis 2020 102 Technological Forecasting and Social Change Q1 9 (Martínez- Climent et al., 2018) Financial return crowdfunding: literature review and bibliometric analysis 2018 87 International Entrepreneurs hip and Management Journal Q1 10 (J. J. Xu & Chau, 2018) Cheap Talk? The Impact of Lender-Borrower Communication on Peer-to- Peer Lending Outcomes 2018 72 Journal of Management Information Systems Q1 5340 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5336-5348, 2024 DOI: 10.55214/25768484.v8i6.3190 © 2024 by the authors; licensee Learning Gate 3.3. Frequency Distribution of Articles by Research Setting Table 2 shows the article distribution frequency based on the research place. The table provides data on understanding P2P topics based on the distribution of country deployments. The highest research was conducted in China, and it had 89 articles. Research on P2P topics is the most extensive and exciting interest carried out by China, followed by United States, which produces 53 articles, and Indonesia, which has 21 articles. China is the country that researches the most on the topic of P2P lending, followed by Indonesia and the United States. Chinese writers have been significant contributors to the P2P lending campaign. The high research interest of academics and practitioners in China to understand the market dynamics and financial implications of P2P Lending is driven by several factors including China has one of the largest P2P lending markets in the world. Rapid economic growth and broad internet penetration are creating a massive demand for alternative financial solutions such as P2P lending. Great support from the government for the development of the P2P lending industry through various policies and incentives. Several P2P lending platforms originating in China have become global leaders in the industry. The challenges and successes facing the platform could spark research interest from Chinese authors to dig deeper into the factors that influence the success and sustainability of its business. In addition, China is also known as a fintech innovation center. Table 3. Frequency distribution of article by research setting. Countries N Countries N China 89 Iran 3 United States 53 Ireland 3 Indonesia 21 Japan 3 United Kingdom 16 Netherlands 3 Hong Kong 11 Poland 3 South Korea 9 Russian Federation 3 Australia 8 Sweden 3 Italia 7 Austria 2 France 6 Denmark 2 German 6 Finland 2 Spain 6 Greece 2 Taiwan 5 Macao 2 Pakistan 4 Malaysia 2 Singapore 4 South Africa 2 Hungary 3 Other country 10 India 3 3.4. Frequency Distribution of Articles by Research Method The dissemination of articles based on research methods can be seen in Table 3. The table lists four categories of research methods: quantitative, qualitative, mixed methods, and literature review. Quantitative research methods are the most frequently conducted among the three on the P2P topics reviewed. This conclusion is obtained in Table 3, which shows a total of 128 (82.6%) quantitative research methods with an archive method classification of 99 (63.9%), surveys 18 (11.6%), and experiments 11 (7.1%) from a total of 202 articles. A research method rarely done in P2P research is a mixed method with 2 (1.3%) articles. 5341 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5336-5348, 2024 DOI: 10.55214/25768484.v8i6.3190 © 2024 by the authors; licensee Learning Gate Table 4. Frequency distribution of article by research method (n=202). Type Quantitative (N/%) Qualitative (N/%) Mix method (N/%) Literature review (N/%) Quantitative Archival 125 (61.8) - - - Survey 28 (13.8) - - - Descriptive - - - - Experiment 10 (4.9) - - - Total quantitative 163 (80.5) - - - Qualitative Interview - 7 (3,4) - - Case study - 11 (5,4) - - Total qualitative - 18 (8,9) - - Mixed method Archival and interview - - 2 (0.9) - Archival, survey, review, and interview - - 2 (0.9) - SLR, archival, and case studies - - - - Total mixed methods - - 4 (2.6) - Literature review - - - 5 (3.2) Total 128 (82.6) 18 (11.6) 4 (2.6) 5 (3.2) 3.5. Frequency Distribution of Articles by Theories Applied in the Research Table 4 shows the theory used in research on P2P topics in 202 reviewed articles. From the data displayed, most P2P topics are done by not conveying theories explicitly in the article, which is as many as 67 articles. Of the total 100 theories in P2P research, asymmetry information theory is the most widely used theory to strengthen opinions and as an author's guide, which is as many as 22 articles, followed by the theory of Technology Acceptance Model (TAM) as many as six articles, and Theory of Planned Behavior (TPB) used in five articles. Asymmetry information theory is used by (C. W. S. Chen et al., 2019; S. Chen et al., 2020b; de Franco et al., 2021; Ding et al., 2019; D. Kim et al., 2020; Klein et al., 2021; J. Li et al., 2021; J. Li & Hu, 2019; Mac an Bhaird et al., 2019; Nisar et al., 2020; Ribeiro- Navarrete et al., 2022; Santoso et al., 2020; Shao & Bo, 2022a; Si et al., 2020; Q. Wang et al., 2021; Weng & Luo, 2021; Xia et al., 2020; J. Xu et al., 2020; H. Yang et al., 2020; Yao et al., 2019; Zanin, 2020; Zhou & Wei, 2020). Theory of Technology Acceptance Model (TAM) presented by (Davis, 1989) used in the article (Azman et al., 2020; Candra et al., 2020a; Darmansyah et al., 2020; Dospinescu et al., 2021b, 2021a; Ichwan & Kasri, 2019; Kumra et al., 2021). Theory of Planned Behavior (TPB) stated by several researchers in their articles, including: (Darmansyah et al., 2020; Ichwan & Kasri, 2019; Kumra et al., 2021; Solihat, Hamundu & Wahyu, 2023; Tang et al., 2020). Table 5. Top ten of theories applied in the research. Theory N Frequency Asymmetry information 22 10.0% Technology acceptance model (Tam) 6 2.7% Theory of planned behavior (TPB) 5 2.3% Prospect theory 4 1.8% The social capital theory 3 1.4% Signaling theory 3 1.4% Theory development 3 1.4% Contract theory 3 1.4% 5342 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5336-5348, 2024 DOI: 10.55214/25768484.v8i6.3190 © 2024 by the authors; licensee Learning Gate Theory N Frequency Decision making 3 1.4% Theory of reasonable action 3 1.4% 3.6. Bibliometrics Analysis Figure 3 reveals the outcomes of a bibliometric analysis using the keywords of the 202 authors. The size of the circle around a term indicates how frequently it appears in an article's keywords. The colors, however, represent groups that indicate how often specific terms in an article appeared together the nearer. The more closely related the words are, the thicker the linking line. The topic of peer-to-peer lending is discussed in this study. Hence, the term frequently occurs is peer-to-peer lending, represented by the largest circle in Figure 3 and depicted in yellow. There are six groups of topics, each of which has a unique hue. Cluster 1 (pink), called financial sector dynamics, comprises seven items: banking, financial crisis, financial market, financial services, investment, lending behavior, and survival analysis—cluster 2 (green) algorithmic decision framework. The set comprises seven elements: credit scoring, decision- making, finance, forecasting, machine learning, peer-to-peer networks, and soft information. Cluster 3 (blue), connected business InfoTech, comprises four elements: asymmetric information, business development, interest rate, and Internet. Yellow serves as the symbol for Cluster 4, which focuses on the interaction of risk and technology. It includes topics such as asymmetric information, business development, interest rates, and the Internet. Cluster 5, represented by purple, is a peer-to-peer market crowdfunding center encompassing commerce, crowdfunding, emerging markets, and investments. Cluster 6, represented by light blue, is an alliance focused on financial innovation. It comprises commercial banks, internet finance, and microfinance institutions. Figure 3. Network visualization. 5343 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5336-5348, 2024 DOI: 10.55214/25768484.v8i6.3190 © 2024 by the authors; licensee Learning Gate 3.7. Existing Research Findings and Discussion Figure 3 illustrates that prior studies on P2P lending were categorized into six distinct clusters. However, this paper will specifically examine 2 clusters garnered significant attention from previous researchers: the financial sector dynamic cluster and the decision framework algorithm cluster. 3.7.1. Financial sector dynamics Financial sector dynamics refer to the changes, movements, and activities occurring within the financial sector of an economy. This refers to the complex interaction of various elements within the financial system, including financial institutions, financial markets, financial products, and other economic organizations. Various factors, including monetary policy, financial regulations, economic volatility, improvements in financial technology, and changes in consumer behavior can impact the dynamics of the financial sector. Prior studies in the financial sector have focused on the behavioral aspects of P2P lending, examining the behavior of both lenders and borrowers. The study conducted by (Shao & Bo, 2022b) presents compelling data demonstrating that behavioral aspects exhibited by P2P platforms greatly influence the P2P lending industry in China. The impact on behavior becomes more significant when the problem of asymmetric knowledge is more severe between investors and P2P platforms. A study ( Ichwan & Kasri, 2019) analyzed the determinants that impact the inclination of young individuals, specifically millennials, to engage in peer-to-peer (P2P) lending. Based on a sample of around 400 young participants, the study's findings suggest a positive correlation between investment intention in P2P lending and attitude characteristics, indicating that the latter influences the former. The attitude variables are influenced by perceived ease of use, knowledge, and trust in P2P lending. According to (Gonzalez, 2020), investor judgments in funding loans are also influenced by investors' experience of financial trauma. Investors who have experienced financial stress are more inclined to engage in herding behavior and provide larger loans to borrowers whom other investors trust. Researchers are also concerned about the factors influencing borrowers' interest in P2P lending. The study conducted by (Solihat, Hamundu, Hendrian, et al., 2023) examined the factors that impact small and medium enterprises (SMEs) in Indonesia through peer-to-peer (P2P) lending. The findings revealed that five variables, including performance expectancy, social influence, price value, hedonic motivation, and effort expectancy, substantially influenced MSMEs. Another factor that influences interest in using P2P lending is attitude (Liang & Chi, 2021), perceived risk (Nguyen & Huynh, 2018), (Lee & Sohn, 2021), (E. C. L. Yang & Nair, 2014), perceived usefulness and perceived ease of (Nguyen & Huynh, 2018), (Widyanto et al., 2022); trust (Pavlou, 2003), (D. J. Kim et al., 2008), (Q. Yang & Lee, 2016), e trust(Candra et al., 2020b). Furthermore, the research identifies characteristics contributing to individuals' reluctance to utilize P2P lending. The study by (Ali et al., 2023) investigated the determinants of Indonesian individuals' avoidance of P2P lending. The findings revealed that the perceived level of danger associated with P2P lending is impacted by its perceived seriousness, perceived vulnerability, and risk tolerance. These perceived hazards and societal forces contribute to individuals' urge to avoid them. 3.7.2. Algorithm Decision Framework An algorithmic decision framework is a systematic collection of techniques to support algorithmic decision-making or computational calculations. An organized approach or framework that enables decision-making by utilizing algorithms or mathematical tools. Technology-driven financial services, like peer-to-peer Lending, need to establish a scoring technique that accurately differentiates between borrowers who are likely to be creditworthy and those who are not before granting credit (Calabrese et al., 2019). Credit scoring is a crucial procedure for peer-to-peer (P2P) lending institutions since it enables them to assess the likelihood of loan applicants defaulting on their payments (Shen et al., 2020). Some researchers have proposed models for credit decision-making for borrowers on P2P lending platforms. Some researchers have proposed models for credit decision-making for borrowers on P2P lending platforms. (Z. Wang et al., 2018) propose a distinctive behavioral scoring model for predicting the changing likelihood of default in peer-to-peer (P2P) lending. Their approach utilizes a framework 5344 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5336-5348, 2024 DOI: 10.55214/25768484.v8i6.3190 © 2024 by the authors; licensee Learning Gate for mixture survival analysis. A random forest model assesses the possibility of a borrower defaulting on their loan. Additionally, a random survival forest model is included to forecast the duration of time before the borrower fails. A study using a dataset of Chinese peer-to-peer loans showed that the ensemble mixture random forest (EMRF) model is better at predicting the monthly dynamic probability of default than the standard mixture cure model, the Cox proportional hazards model, and logistic regression–which was what was suggested. Furthermore, it has been established that the EMRF model presented yields a pertinent result for the prompt handling of post-loan risk. (Shen et al., 2020) propose a cost-sensitive logistic regression credit scoring model that utilizes a multi-objective optimization technique. The cost-sensitive logistic regression model has two purposes. Multiple objective particle swarm optimization (MOPSO) determines the cost-sensitive logistic regression parameters. (Calabrese et al., 2019) Introduce a new bivariate regression model called R BivGEV to analyze the correlation between defaults in P2P lending and credit bureaus. Compared to bivariate probit and univariate logit models, this one is better at predicting P2P defaults, estimating the expected loss, and figuring out the value at risk. The study conducted (Babaei & Bamdad, 2020) presents a credit assessment model designed explicitly for novice lenders. The aim is to enhance decision-making models by employing net present value (NPV) and integrating supervised learning and optimization methodologies to attain the desired outcomes. To determine the most suitable artificial neural network (ANN) model for predicting net present value (NPV), three distinct ANN models are assessed using metrics such as mean square error, mean absolute error, and root-mean-square error. Furthermore, to conduct a risk assessment, the probability of loan default is determined using logistic regression calculations. (Z. Wang et al., 2018) presented a novel text mining method to extract semantic soft components from descriptive loan texts automatically. Associating concepts with an embedding space, grouping terms with similar meanings into semantic clusters, and then giving these semantic clusters semantic soft factors are all parts of this approach. The results of the empirical study show that the retrieved semantic soft factors greatly enhance credit risk assessments in terms of both their ability to discriminate and their ability to lend. The study by (Croux et al., 2020) has identified a comprehensive collection of contractual loan features, borrower characteristics, and macroeconomic variables that are crucial in influencing the probability of default. These factors include loan maturity, homeownership, loan purposes, and occupation. (Guo et al., 2021) introduces a maturity-based lender composition score. This score leverages the investing capacity of a consortium of lenders pooling their resources to enhance the assessment of peer-to-peer loans. The researchers examined the investing histories of lenders to identify their performance, risk, and experience profiles in a more detailed manner. In addition, they constructed our loan evaluation indicators by combining lender profiles in the composition. By incorporating interim lenders, they successfully documented this advancement and included it in the consolidation procedure. This enabled them to measure a lender's potential for continuous enhancement in peer-to-peer investments. Empirical research has shown that maturity-based lender composition scores can be helpful indicators for determining loan quality. These scores can be incorporated into existing loan evaluation models to enhance accuracy. The study by (Aleksandrova, 2021) aimed to analyze several prominent machine learning algorithms to assess their effectiveness in improving credit ratings for peer-to-peer loans. Various models have been employed, including individual classifiers (such as logistic regression, decision trees, and multilayer perceptron), uniform ensembles (such as XGBoost, GBM, and Random Forest), and diverse ensemble classifiers like stacked ensembles. The results indicate that ensemble classifiers outperform single classifiers, with XGBoost and Stack Ensemble being particularly successful methods. Bussmann et al. (2021) proposed an artificial intelligence model for credit risk management. This model was purposefully developed to assess the risks of borrowing funds via peer-to-peer lending networks. This model may offer a detailed explanation for each prediction by quantifying the contribution of each explanatory variable in terms of the Shapley value. This explanation can be provided from a substantive standpoint. The authors utilized TreeSHAP, a dependable and uncomplicated method available in open-source packages. TreeSHAP is an efficient technique that can calculate the Shapley Additive Explanation for trees in polynomial time instead of the usual exponential runtime. The boost component of the model benefited from the utilization of NVIDIA graphics 5345 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 5336-5348, 2024 DOI: 10.55214/25768484.v8i6.3190 © 2024 by the authors; licensee Learning Gate processing units (GPUs) to significantly accelerate computations. The TreeSHAP technique efficiently extracted data from the xgboost model using this strategy. (Woo & Sohn, 2022) introduced an alternative credit scoring model designed explicitly for peer-to-peer Lending. This model was created by identifying common personality traits inferred from the borrower's occupation. By employing an affinity matrix built from Myers-Briggs type indications (MBTI) tailored to each job category, they projected the virtual world of borrowers. It was found that each MBTI type is unique and separate from the others. The credit scoring model incorporates diverse critical criteria into its roster. The study's findings illuminate notable progress in developing alternative credit rating methodologies for peer-to- peer lending. The study by (Lyócsa et al., 2022) introduces a credit assessment method that utilizes a profit- scoring strategy and is founded on the modeling of the yearly adjusted internal rate of return on loans. Diverges dramatically from the previous study conducted. Based on the research findings, the profit scoring model yields a higher rate of return than the credit risk scoring model. 4. Conclusion The objective of this literature study is to ascertain the advancements made in prior research on the topic of P2P lending. According to Scimago, a total of 202 papers have been assessed by exclusively considering empirical studies published in credible journals. This encompasses the years 2017 and 2023. P2P subjects are intriguing due to the prevalence of articles with the highest quality index (Q1). 43% of the items have the highest quality index (Q1). The articles published with a medium quality index (Q2) and (Q3) account for 32% and 19% of the total articles, respectively. Compared to other articles, just 6% of articles on P2P issues have a low-quality index (Q4), indicating a small quantity of such articles. Articles on P2P themes are distributed throughout 31 nations, including articles conducted in 3 countries and two countries, respectively, and some articles without specified study locations. China did the most extensive research, with a total of 67 papers. The article's keyword grouping identified six cluster themes: Financial sector dynamics, Algorithm decision framework, connected business InfoTech, P2P Market Crowdfunding Hub, and financial innovation. This evaluation has various constraints. Initially, Scopus served as the sole database for article identification in this review. Furthermore, the review did not incorporate many inaccessible items. Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] A. Basha, S., Elgammal, M. M., & Abuzayed, B. M. (2021). Online peer-to-peer lending: A review of the literature. Electronic Commerce Research and Applications, 48(April), 101069. https://doi.org/10.1016/j.elerap.2021.101069 [2] Aleksandrova, Y. (2021). Comparing Performance of Machine Learning Algorithms for Default Risk Prediction in Peer to Peer Lending. 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