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Lenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns with risk limitations or lowering risks with expected returns for P2P lenders.

This paper used data from a leading online P2P lending platform in America. First, the authors constructed a logistic regression-based credit scoring model and a linear regression-based profit scoring model to predict the default probabilities and profitability of loans. Second, based on the predictions of loan risk and loan return, the authors constructed linear programming model to form the optimal loan portfolio for lenders.

The research results show that compared to a logistic regression-based credit scoring method, the proposed new framework could make more returns for lenders with risks unchanged. Furthermore, compared to a linear regression-based profit scoring method, the proposed new framework could lower risks for lenders without lowering returns. In addition, comparisons with advanced machine learning techniques further validate its superiority.

Unlike previous studies that focus solely on predicting the default probability or profitability of loans, this study considers loan allocation in online P2P lending as an optimization research problem using a new framework based upon modern portfolio theory (MPT). This study may contribute theoretically to the extension of MPT in the specific context of online P2P lending and benefit lenders and platforms to develop more efficient investment tools.

Innovation in financing processes, enabled by the advent of new technologies, has supported the development of alternative finance funding tools. In this context, the study analyses the growing importance of alternative finance instruments (such as equity crowdfunding, peer-to-peer (P2P) lending, venture capital, and others) in addressing the small and medioum enterprises' (SMEs) financing needs beyond traditional bank and market-based funding channels. By providing more flexible terms and faster approval times, these instruments are gradually reshaping the traditional bank-firm relationship.

To comprehensively understand this innovation shift in funding processes, the study employs a novel approach that merges three MCDA methods: Spherical Fuzzy Entropy, ARAS and TOPSIS. These methodologies allow for handling ambiguity and subjectivity in financial decision-making processes, examining the effects of multiple criteria, including interest rate, flexibility, accessibility, support, riskiness, and approval time, on the appeal of various financial alternatives.

The study’s results have significant theoretical and practical implications, supporting SMEs in carefully evaluate financing alternatives and enables banks to better identify the main “competitors” according to the “financial need” of the firm. Moreover, the rise of alternative finance, notably P2P lending, indicates a shift towards more efficient capital access, suggesting banks must innovate their funding channels to remain competitive, especially in offering flexible solutions for restructuring and high-risk scenarios.

The study advises top management that SMEs prefer traditional loans for their reliability and accessibility, necessitating banks to enhance transparency, innovate, and adopt digital solutions to meet evolving financing needs and improve customer satisfaction.

The study introduces a novel integration of Spherical Fuzzy TOPSIS, Entropy, and ARAS methodologies to face the complexities of financial decision-making for SME financing, addressing ambiguity and multiple criteria like interest rates, flexibility, and riskiness. It emphasizes the importance of traditional loans, the rising significance of alternative financing such as P2P lending, and the necessity for banks to innovate, thereby enriching the literature on bank-firm relationships and SME funding strategies.

This study seeks to provide a systematic analysis of bounded rationality expressed by individual lenders in a Peer-to-peer (P2P) lending market.

26,383 personal loan listings collected from Moneyauction in Korea, were analyzed with binary logit regression. 6 hypothesis based on bounded rationality theory were constructed and tested. Binary logit regression was employed as both dependent variables have binary characteristics and can thus be assigned values equal to 0 or 1.

The results confirm that individual P2P lenders make their funding decisions based on bounded rationality, arousing from cognitive limitations, incomplete information, and time constraints.

By adopting the theory of bounded rationality, this study attempts to prepare the theoretical background for an explanation of the decision behavior of individual lenders in a P2P lending market.

The findings of this research emphasize the importance of the platform provider's role to facilitate the sustainable market growth of P2P lending as an alternative form of finance. As the rationality of individual lenders is bounded during their decision-making process according to the research findings, the platform provider must continuously adjust their decision criteria by referencing the cumulative loan repayment data.

This study attempts to identify for the first time the suboptimal decision making by individual lenders in a P2P lending market on the basis of bounded rationality theory.

Owing to the dramatic rise of FinTech credit in the financial sector, this study describes its knowledge and intellectual structure and paves the way for future research.

The study employs citation analysis, keyword analysis, co-author analysis, co-citation analysis and bibliographic coupling on 268 peer-reviewed articles published during 2010–2021 and extracted from the Web of Science database.

Research on FinTech credit has picked up momentum from 2016, with majority contributions from China, followed by UK and USA. International Journal of Bank Marketing is found to be the most productive journal. Co-citation analysis reveals that past studies have focused on three dominant themes, viz. (a) factors that influence user intention to adopt technological products and services (b) borrowers' and lenders' characteristics that impact fund-raising in FinTech credit platforms and (c) evolution of FinTech market over the years. Bibliographic coupling reveals that recent trends in FinTech credit include (a) impact of emerging technologies like blockchain, artificial intelligence, big data on financial system, (b) factors that encourage consumers to adopt the FinTech products and services, (c) mechanisms by which FinTechs have transformed formal credit markets, (d) factors that lead to successful fundraising in FinTech platforms and (e) critical perspectives on digital lending platforms.

To the best of the authors' knowledge, this is a pioneering study undertaking an exhaustive analysis of FinTech credit as a research area. The study offers valuable insights on potential topics of research in FinTech credit domain like investigating Balance Sheet Lending Model, investigating the impact of FinTechs on financial system, and new markets by collaborating with scholars of other regions.

Marketplace lending has substantially changed since the first peer-to-peer lending platforms emerged in 2006. The industry is now an alternative to bank lending, predicted to total $70 billion for consumer and business loans worldwide by 2030. Marketplace lending is often deemed less safe than bank loans, mainly due to these portfolios' high degree of hidden information. These include needing more information on borrowers and potential correlations between them, which might lead to higher risk than is apparent at first glance. Deterministic processes cannot capture tail risk appropriately, so platforms and lenders should employ stochastic processes. This chapter introduces a Monte Carlo simulation and machine learning (ML) process to evaluate and monitor portfolios. For marketplace lending to become a viable and sustainable alternative to bank lending platforms, they must better evaluate, monitor, and manage tail risk in marketplace loans and develop tools to monitor and manage financial risk losses.

The purpose of this paper is to return to Stinchcombe’s original emphasis on emerging vs existing organizations by examining the cognitive legitimacy challenges aspiring entrepreneurs face vis-à-vis entrepreneurs with existing businesses.

The data collection included content analysis of profiles of an online crowdfunding, peer-to-peer lending market leading to a sample of 507 business loan requests, 123 of which were requests to support new business ideas rather than existing businesses. Negative binomial regression was used to test hypotheses regarding whether aspiring entrepreneurs seeking convenience-based support for their new business ideas would be less successful than their counterpart entrepreneurs seeking support for their existing businesses.

The findings show that aspiring entrepreneurs received less convenience-based support for their new business ideas from key resource providers than their peer entrepreneurs asking for support for existing businesses. The findings also suggest that this liability of newer than newness may be able to be mitigated by reputational signals such as the creditworthiness of the entrepreneur making the request.

This study focuses on the original insights Stinchcombe introduced when he described the social conditions that produce the liability of newness. Moreover, this study offers explicit theory as to the key mechanisms that cause the liability of newness by focusing on an aspiring entrepreneur’s ability to secure convenience-based support and potential ways an aspiring entrepreneur may offset that liability.

In recent years, the penetration of digital technologies in the financial industry determined the arising of Fintech, which generated a dynamic and rapid change that business operators and supervisory authorities in the banking industry are struggling to follow it. This is especially due to issues affecting financial intermediaries and customers, and potential risks of stability of the financial system. The aim of this paper is to provide a review of Fintech in the banking industry thus to update the knowledge about technology innovation in the banking sector, identify the major trends in the domain and delineate future research directions.

The study reviews 377 articles indexed on Scopus from 2014 to 2021 that focus on Fintech and the banking industry. The methodology adopted is structured in two steps: the keywords selection and the analysis of the documents extracted. The first step identified “Fintech” and “bank” as keywords to be searched within the title, abstract or keywords of documents indexed on Scopus; whereas the second step combined R and VOSviewer to provide a descriptive analysis of the dataset and the analysis of keywords and occurrences, respectively.

Results achieved in the study allow providing a systemic view of the Fintech in the banking industry, including the emergent phenomenon of digital banking. In particular, it is provided with a general overview and descriptive information on the entire sample of documents analyzed, their authors, the keywords used and the most cited works. Besides, a deepening on the model of digital banking is provided, by delineating the six dimensions of the key effects generated by the digital bank model.

Two main elements of originality characterize this study. The first one is related to the fact that few review studies have been published on Fintech in the banking industry, and the second one concerns the multiple dimensions of the impact of Fintech in the banking sector, which includes customer, company, bank, regulation authority and society.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

Crowdfunding is an emergent practice that is increasing exponentially as a means of financing to complement company capital. This chapter focuses on an innovative way of organizing peer-to-peer lending, known as crowdlending. The characteristics of crowdlending are social reward or interest and using the Internet as a medium for communication, prospection and raising funds. To fill the gap in the literature in this regard, this chapter addresses the following questions: Can crowdfunding be considered as a feasible conventional financial tool? What makes crowdlending work? Is it possible to apply the mutual cash holding (MCH) model to crowdlending as well as to previous examples such as the Mondragon Corporation and Trocobuy?

We use three cases (Mondragon Corporation, Trocobuy and Arboribus) to highlight financial tools that use the concept of stakeholder theory that is based on the collaborative management of cash surpluses. Using the Delphi technique combined with in-depth interviews we demonstrate the contribution of the MCH model to crowdlending. We show that the model could be applied to different organizations, thereby indicating its robustness and implying that it could be used in many other cases.

The present study suggests that crowdlending describes a new financing tool as a principal form of lending; it enables companies to implement a financial tool that allows for social development and stakeholder participation and that can ensure companies’ financial sustainability.

This model is based on six elements: expectations of mutual benefits, trust, management, guarantees, mutual profit and benefit. It suggests mutual benefit and positive social values for all stakeholders. However, cash surpluses will be efficiently used only when crowdlending is relevant to investors’ economic objective, because crowdlending as a social innovation does not in itself guarantee economic benefit.

The chapter provides evidence of crowdlending in practice. The research compares key cases in which the MCH model is applied. It also provides important insights into crowdlending as a social innovation.