• Journal of the Korea Society of Computer and Information
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• v.22 no.6
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• pp.141-147
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• 2017

Along with the recent growth of Fintech industry and low interest rate basis, one of the alternative investment technique for expecting higher investment profit, P2P loan using P2P financial system is greatly increasing. P2P loan can be referred to as a type of Crowdfunding that the law of Crowdfunding (adopted to revised Capital Market Act) enacted on January 25th 2016 only allows investment type Crowdfunding so that it can be used as a tool of raising fund for startup and venture companies. Also, it is true that Korean government could not make any legislative foundation related to P2P loan. At this moment, those online platform companies mediating P2P loan are not included as financial companies, expected to cause various legal arguments. Financial Services Commission has released a guideline in February of this year saying that limit of P2P loan is 10 million Korean Won per arbitrating company and 5 million Korean Won per borrower. However, what is more important is to make a law supporting this institutional system. If legislation on P2P loan is implemented without care, it may disturb growth of the field but it may result in the damage of investors if not clearly defined by law. As this is the case, first, "revision of execution regulations for loan business" should take place as soon as possible to intensify inspection of loan companies by registering them to Financial Services Commission. Second, saving customer fund separately in the their organization. Third, making law on protecting investors such as regulating exaggerative advertisement. Fourth, to have transparent and fair public announcement system, standardized agreement and guideline describing clear understanding on autonomous public information publication of P2P loan online platform business and information on the borrower.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.207-224
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• 2018

In this article, an empirical study was conducted by using public dataset from Lending Club Corporation, the largest online peer-to-peer (P2P) lending in the world. We explore significant predictor variables related to P2P lending default that housing situation, length of employment, average current balance, debt-to-income ratio, loan amount, loan purpose, interest rate, public records, number of finance trades, total credit/credit limit, number of delinquent accounts, number of mortgage accounts, and number of bank card accounts are significant factors to loan funded successful on Lending Club platform. We developed online P2P lending default prediction models using discriminant analysis, logistic regression, neural networks, and decision trees (i.e., CART and C5.0) in order to predict P2P loan default. To verify the feasibility and effectiveness of P2P lending default prediction models, borrower loan data and credit data used in this study. Empirical results indicated that neural networks outperforms other classifiers such as discriminant analysis, logistic regression, CART, and C5.0. Neural networks always outperforms other classifiers in P2P loan default prediction.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.79-94
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• 2013

In this paper, we examine factors of success in online P2P (peer-to-peer) lending auctions. This paper finds the following empirical results. First, loan applicants with a stable employment status are more likely to succeed in the auction than loan applicants with an unstable employment status. Second, loan applicants, who actively share personal information and interact with lenders through online message boards, are likely to succeed in the auction. Third, the purpose of a loan for debt repayment has a significant impact on the success of the auction. However, the purpose of a loan for essential living expenses such as housing, living, and medical expenses has an insignificant relationship with the success of the auction. Our results imply that the characteristics of loan applicants such as employment status and social interaction are the factors of success in online P2P lending auctions.

• Knowledge Management Research
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• v.21 no.3
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• pp.229-247
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• 2020

In the remarkable growth of P2P financial platform in the field of knowledge management, only companies with big data and machine learning technologies are surviving in fierce competition. The ability to analyze borrowers' credit is most important, and platform companies are also recognizing this capability as the most important business asset, so they are building a credit evaluation system based on artificial intelligence. Nonetheless, online P2P platform providers that offer related services only act as intermediaries to apply for investors and borrowers, and all the risks associated with the investments are attributable to investors. For investors, the only way to verify the safety of investment products depends on the reputation of P2P companies from newspaper and online website. Time series information such as delinquency rate is not enough to evaluate the early stage of Korean P2P makers' credit analysis capability. This study examines the credit analysis procedure of P2P loan platform using artificial intelligence through the case analysis method for well known the top three companies that are focusing on the credit lending market and the kinds of information data to use. Through this, we will improve the understanding of credit analysis techniques through artificial intelligence, and try to examine limitations of credit analysis methods through artificial intelligence.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.129-138
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• 2021

Several studies have revealed that information on borrower characteristics plays an important factor in approving their credit requests. Though the extent to which such characteritics are also applicable to the case of fintech lending remain uncertain. The aim of this study is, thus, to investigate the determinant factors that influence MSEs in obtaining credit through fintech lending. Here, we emphasize virtual trust in fintech lending encompasing the dimension of social network, economic attributes, and risk perception based on several indicators that are used as proxies. Primary data used in the study was gathered from an online survey to the respondents of MSEs in Java. The result of the study indicates that determinants of MSEs in obtaining credit from lender through fintech lending are statistically influenced by internet usage activities, borrowing history, loan utilization, annuity payment system, completeness of credit requirement documents and compatibility of loan size with the business need. These factors have a significant effect on credit approval because they can generate virtual trust of fintech lender to MSEs as potential borrowers. It concludes that the probability of obtaining fintech loans in accordance with their expectations are influenced by the dimensions of social network, economic attributes and risk perception.

• Industry Promotion Research
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• v.7 no.4
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• pp.109-119
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• 2022

In recent years, China's Internet finance industry is hot. There is no doubt that Internet finance has been fully integrated into China, forming a new form of financing, and rapidly becoming a new channel for investment and financing in China, shouldering the responsibility of inclusive financing and building China's real economy. However, with investment, there are risks. Based on the panel data of China's Internet financial platform, this paper uses the random effect model to study the influencing factors of Internet financial risks, and draws three conclusions: (1) The user funds and platform funds of the financial platform will be managed separately by the bank, which can effectively reduce the risk of financial transactions on the Internet; (2) The risk of Internet financial transactions can be effectively reduced by avoiding the concentration of platform funds in the hands of a few borrowers through regulatory policies; (3) The liquidity control of funds effectively reduces the risk of Internet financial transactions. Based on the conclusions, we propose optimization strategies for regulatory policies to achieve the healthy and sustainable development of Internet finance.