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

Cluster analysis of development of alternative finance models depending on the regional affiliation of countries

The article examines the hypothesis about the existence of regional peculiarities in the development of alternative financing models (such as p2p consumer lending, p2p business lending, p2p real estate lending, balance sheet business lending, balance sheet consumer lending, equity-based crowdfunding, reward-based crowdfunding, real estate crowdfunding, profit sharing crowdfunding, donation-based crowdfunding, invoice trading, debt-based securities). According to an alternative hypothesis, due to the high integration of international financial markets, there are no regional peculiarities of the development of alternative financing models. The cluster analysis tools allow verifying these hypotheses. The cluster analysis methods used, such as tree clustering, k-means clustering, and two-way joining, demonstrate the lack of links between the country's regional affiliation and the degree of development of certain types of alternative financing in it. The key factors affecting the formation of clusters are volumes of peer-to-peer consumer lending and business lending, as well as the volume of invoice trading. According to the results of the research, the authors conclude that it is necessary to find other factors, apart from the regional features, which influence the ratio in the development of certain types of alternative financing in different countries

Does Gender Affect Investors' Appetite for Risk?: Evidence from Peer-to-Peer Lending

This study investigates the role of gender in financial risk-taking. Specifically, I ask whether female investors tend to fund less risky investment projects than males. To answer this question, I use real-life investment data collected at the largest German market for peer-to-peer lending. Investors' utility is assumed to be a function of the projects expected return and its standard deviation, whereas standard deviation serves as a measure of risk. Gender differences regarding the responses to projects' risk are tested by estimating a random parameter regression model that allows for variation of risk preferences across investors. Estimation results provide no evidence of gender differences in investors' risk propensity: On average, male and female investors respond similarly to the changes in the standard deviation of expected return. Moreover, no differences between male and female investors are found with respect to other characteristics of projects that may serve as a proxy for projects' risk. Significant gender differences in investors' tastes are found only with respect to preferred investment duration, purpose of investment project and borrowers' age.gender, investment choice, risk preferences

Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors

In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendations are becoming popular to explore the temporal characteristics of customers' interactive behaviors. However, existing works mainly exploit the short-term behaviors without fully taking the customers' long-term stable preferences and evolutions into account. In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations. Specifically, BINN contains two main components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning. Firstly, a novel item embedding method based on user interactions is developed for obtaining an unified representation for each item. Then, with the embedded items and the interactive behaviors over item sequences, BINN discriminatively learns the historical preferences and present motivations of the target users. Thus, BINN could better perform recommendations of the next items for the target users. Finally, for evaluating the performances of BINN, we conduct extensive experiments on two real-world datasets, i.e., Tianchi and JD. The experimental results clearly demonstrate the effectiveness of BINN compared with several state-of-the-art methods.Comment: 10 pages, 7 figures, KDD 201

The emergence of the fintech industry in China: An evolutionary economic geography perspective

Over the last decade, the global economy has rapidly becoming digited. Digital technologies have transformed the economy and society, affecting all sectors of activity around the world. Among them, the financial sector is one of the most digitalized sectors, and the term ‘fintech’ is coined to describe the digitalization of the financial sector. Although the global fintech landscape is currently geographically concentrated in the United States and Europe, the pace of China’s fintech development has been dramatically accelerated. However, it is quite surprising that there is hardly any study that investigates fintech in China from a subnational scale. To fill this gap, this dissertation conducts a city-level analysis of the emergence of the fintech industry in China. Theoretically, I position this dissertation within the broad literature on evolutionary economic geography (EEG), which has emerged as one of the main paradigms in economic geography. This dissertation aims to provide a comprehensive understanding of the emergence of the new industry in regions. Conventional wisdom in EEG posits that new industry in regions tends to grow out of technologically related pre-existing industries. However, this conventional understanding is somewhat technology-centric. In response, this dissertation extends the scholarly work from technology-centric to embrace the role of the demand-side market and institutional logic in the emergence of the new industry in regions. It proposes that not only supply-side technology but also demand-side market and institutional logics matter for the emergence of the new industry in regions. Moreover, this dissertation ascribes the underlying logic of how technology, market, and institutional logics matter to the agentic processes of asset modification, particularly redeploying pre-existing assets and creating new assets. In other words, the emergence of the new industry in regions results from relevant regional actors’ purposeful actions in terms of modifying technological, market, and institutional assets. Methodologically, there is a dualism in evolutionary economic geography research between qualitative and quantitative work. To seek a methodology integration, this dissertation proposes the mixed-method that is composed of four concrete approaches, namely the triangulation approach, the embedded approach, the sequential exploratory approach, and the sequential explanatory approach. Among these concrete approaches, the embedded approach is utilized in empirical work. The embedded approach in this dissertation refers to the embedding of the qualitative case study (which deals with the ‘how’ questions) into quantitative research (which deals with the ‘whether and to what extent’ questions). Empirically, this dissertation first examines the emergence of fintech industries in China’s cities based on the quantitative regression analysis (mainly dealing with the ‘whether and to what extent’ questions) and then zooms in on the city of Shenzhen, which is the largest fintech hub in southern China, based on the qualitative case study (mainly dealing with the ‘how’ questions). The findings are as follows. (1) Based on a unique dataset from 2003 – 2019, this dissertation provides a city-level analysis of the fintech industry in China. The econometric results show that fintech industries tend to emerge in cities that have more fintech-related technologies, particularly in the fields of finance, e-commerce, data sciences, and security. This confirms the principle of technological relatedness. Moreover, it finds a positive relationship between the development of the fintech industry and the demand for fintech services. To the best of my knowledge, this is the first systematic evidence of the significant positive role of the demand-side market in the emergence of the new industry in regions. (2) In order to uncover the underlying processes (the question of ‘how’) that lead to the above significantly positive effect, this dissertation resorts to the qualitative case study. The case study shows that the rise of Shenzhen’s fintech industry mainly grows out of Shenzhen’s pre-existing internet and financial industry. By systematically comparing the processes that internet and financial industry diversify into the fintech industry, it finds that the emergence of the fintech industry in Shenzhen result from internet and financial firms’ purposeful actions in terms of redeploying their pre-existing technologies, market, and institutional logics, as well as creating the new ones that are necessary for fintech but are missing for the internet or financial firms. In other words, it is the processes of asset modification, particularly redeploying pre-existing assets and creating new assets, that give rise to the birth of the fintech industry, leading to the positive relationships found in the quantitative regression analysis

FinTech, RegTech and the role of alternative lending: an analysis of the P2P platform LendingClub

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