• 한국컴퓨터정보학회논문지
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• 제22권6호
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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.

• 한국전자거래학회지
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• 제23권3호
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• pp.207-224
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• 2018

온라인 P2P 대출(Online Peer-to-Peer Lending)이란 대출자(차입자)들이 인터넷 및 모바일 P2P 플랫폼을 통해 대출을 신청하면 P2P 플랫폼 기업이 이를 심사하고, 공개하여 불특정 다수가 자금을 빌려주고 이자를 받는 대출중개 서비스를 말한다. 국내외적으로 P2P 대출시장의 성장과 수익률에 대한 관심이 커진 상황에서 현재는 P2P 대출에 대한 안정성 측면에서 문제가 제기되고 있다. P2P 대출시장은 높은 수익률을 제공하지만 P2P 업체의 연체율과 부실률(채무불이행률)도 함께 높아지고 있는 실정이다. P2P 금융시장의 신뢰도를 높이기 위해서는 P2P 대출의 연체율과 채무불이행률을 줄이는 것이 무엇보다 중요하다. 본 연구는 세계적인 P2P 기업인 렌딩클럽(Lending Club)의 P2P 대출거래데이터베이스를 이용하여 인공지능기반의 P2P 채무불이행 예측모형을 구축하고자 한다. 구체적으로 벤치마크(benchmark) 모형으로 통계기법인 판별분석과 로지스틱 회귀분석을 이용하고, 인공지능기법으로는 신경망, CART, 그리고 C5.0을 이용하여 P2P 대출거래의 채무불이행 예측모형을 구축하고자 한다. 연구결과, P2P 대출거래의 채무불이행 예측을 위해 우선 고려해야 할 변수는 대출이자율이며, 중요도 3순위에 가장 많이 언급된 대출금액과 총부채상환비율도 고려해야 할 요인으로 추출되었다. 전통적인 통계기법보다는 인공지능기법의 예측성과가 더 좋은 것으로 나타났으며, 신경망의 경우 모든 데이터 셋에서 오분류율이 가장 낮은 예측모형으로 나타났다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3627-3641
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• 2021

Fintech, which stands for financial technology, is growing fast globally since the economic crisis hit the United States in 2008. Fintech companies are striving to secure a competitive advantage over existing financial services by providing efficient financial services utilizing the latest technologies. Fintech companies can be classified into several areas according to their business solutions. Among the Fintech sector, peer-to-peer (P2P) lending companies are leading the domestic Fintech industry. P2P lending is a method of lending funds directly to individuals or businesses without an official financial institution participating as an intermediary in the transaction. The rapid growth of P2P lending companies has now reached a level that threatens secondary financial markets. However, as the growth rate increases, so does the potential risk factor. In addition to government laws to protect and regulate P2P lending, further measures to reduce the risk of P2P lending accidents have yet to keep up with the pace of market growth. Since most P2P lenders do not implement their own credit rating system, they rely on personal credit scores provided by credit rating agencies such as the NICE credit information service in Korea. However, it is hard for P2P lending companies to figure out the intentional loan default of the borrower since most borrowers' credit scores are not excellent. This study analyzed the voices of telephone conversation between the loan consultant and the borrower in order to verify if it is applicable to determine the personal credit score. Experimental results show that the change in pitch frequency and change in voice pitch frequency can be reliably identified, and this difference can be used to predict the loan defaults or use it to determine the underlying default risk. It has also been shown that parameters extracted from sample voice data can be used as a determinant for classifying the level of personal credit ratings.

• 한국경영과학회지
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• 제38권4호
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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.

• The Journal of Asian Finance, Economics and Business
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• 제7권7호
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• pp.85-93
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• 2020

In peer-to-peer (P2P) loan markets, as most lenders are unskilled and inexperienced ordinary individuals, it is important to know the characteristics of borrowers that significantly impact their repayment performance. This study investigates the effects and importance of borrowers' past repayment performance track record within the platform to identify its predictive power. To this end, I analyze the detailed loan repayment data from two leading P2P lending platforms in Korea using a Cox proportional hazard, multiple linear regression, and logit models. Furthermore, the predictive power of the factors proxied by borrowers' track records are evaluated through the receiver operating characteristic (ROC) curves. As a result, it is found that the borrowers' past track record within the platform have the most important impact on the repayment performance of their current loans. In addition, this study also reveals that the borrowers' track record is much more predictive of their repayment performance than any other factor. The findings of this study emphasize that individual lenders must take into account the quality of borrowers' past transaction history when making a funding decision, and that platform operators should actively share the borrowers' past records within the markets with lenders.

• The Journal of Asian Finance, Economics and Business
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• 제8권10호
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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.

• 지식경영연구
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• 제21권3호
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• pp.229-247
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• 2020

지식경영 분야의 P2P금융 플랫폼의 성장속에서 빅데이터 및 머신러닝(Machine Learning) 기술을 보유한 회사만이 치열한 경쟁 속에서 생존할 가능성이 높을 것으로 예상된다. 그럼에도 불구하고 관련 서비스를 제공하는 온라인 P2P대출 플랫폼 업체들은 투자자와 대출을 신청하는 중개자로서의 역할을 수행할 뿐이며 투자와 관련된 위험은 모두 투자자에게 귀속시키고 있다. 이러한 이유로, 투자자 입장에서는 투자상품의 안전성을 확인할 수 있는 유일한 방법이 신문이나 온라인 웹사이트를 통한 P2P대출 플랫폼 업체의 평판에만 의존할 수 밖에 없는 실정이다. 또한, 한국의 P2P대출 플랫폼 업체들이 대출자의 개별 신용분석을 체계적으로 실시하여 연체율 등의 시계열 정보를 정확히 파악하기에는 시간적, 경제적 여건이 매우 열악한 상황이다. 그러나, 최근 몇몇 P2P대출 플랫폼 업체들이 업체별 대출자 신용분석에 대한 역량을 가장 중요한 영업자산으로 인식함으로써 빅데이터 및 머신러닝 기술을 바탕으로 인공지능(AI)에 기반한 새로운 신용평가 시스템을 구축하고 시행에 들어가고 있음은 매우 긍정적으로 평가된다. 따라서, 본 연구에서는 신용대출 시장에 주력하고 있으며 인공지능 활용으로 잘 알려진 상위 3개 업체를 대상으로 사례분석 방식을 통해 인공지능을 활용한 대출자 신용분석 절차 및 사용하는 정보 데이터의 종류 등을 분석하고자 한다. 이를 통하여 현 상황에서 P2P 플랫폼 업체들의 인공지능을 통한 신용분석 기법을 이해하고 현 시점에서 국내 인공지능을 활용한 신용분석 방식의 한계점과 개선방안 등을 함께 고찰하고자 한다.

• 디지털융복합연구
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• 제20권4호
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• pp.185-192
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• 2022

본 연구는 P2P(Peer-to-Peer) 대출의 부도위험 예측을 위하여 준지도학습(SSL) 기반의 모델을 개발하고자 한다. 검증된 성능에도 불구하고 지도학습(SL) 방법은 완전 지불 또는 채무불이행과 같이 레이블이 결정된 다수의 데이터가 필요한데 충분한 수의 레이블 데이터를 수집하려면 많은 자원과 시간이 필요하다. P2P 플랫폼이 급성장하면서 대출 건수도 매해 급증하였고, 레이블이 없는 데이터도 지속적으로 증가하고 있다. 본 연구는 P2P 대출 플랫폼인 LendingClub에서 수집한 데이터를 사용하였다. P2P 대출 중 레이블이 결정된 대출에서 추출한 정보뿐만 아니라 레이블이 결정되지 않은 대출에서 추출한 정보도 사용하여 부도 위험을 예측하는 SSL 모델을 개발하여 연구를 수행한 결과, 적은 수의 레이블이 결정된 데이터를 사용함에도 불구하고 SSL 방법으로 구축된 모델이 많은 수의 레이블이 결정된 데이터를 사용하여 학습시킨 SL 방법으로 구축된 모델보다 부도 위험 예측성과가 향상되었다.

• 한국빅데이터학회지
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• 제1권1호
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• pp.77-84
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• 2016

최근 이슈가 되고 있는 핀테크 산업의 종류 및 특징에 대해 알아본다. 이를 통해 핀테크 산업의 본질은 플랫폼 사업이라는 것과 시장선점에 있다는 사실을 확인한다. 아울러 핀테크 산업이 성공하기 위해서는 기존 금융서비스보다 단가를 낮추기 위한 방안이 필요하고 이에 대한 해결책은 바로 빅데이터 활용 및 빅데이터 분석임을 인식 한다. 마지막으로 기존 금융권과 신생 핀테크 업체들 간의 상생을 위한 협력만이 우리나라 핀테크가 나아가야할 방향임을 제언한다.

• 전기전자학회논문지
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• 제17권2호
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• pp.182-188
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• 2013

Using a ceramic target ZnO:In with In doping concentration of 2%, hetero-junctions of n-ZnO:In/p-Si and p-ZnO:(In, N)/n-Si were fabricated by depositing Indium doped n - type ZnO (ZnO:In or IZO) and Indium-nitrogen co-doped p - type ZnO (ZnO:(In, N)) films on wafers of p-Si (100) and n-Si (100) by DC magnetron sputtering, respectively. These films with the best electrical and optical properties were then obtained. The micro-structural, optical and electrical properties of the n-type and p-type semiconductor thinfilms were characterized by X-ray diffraction (XRD), RBS, UV-vis; four-point probe resistance and room-temperature Hall effect measurements, respectively. Typical rectifying behaviors of p-n junction were observed by the current-voltage (I-V) measurement. It shows fairly good rectifying behavior with the fact that the ideality factor and the saturation current of diode are n=11.5, Is=1.5108.10-7 (A) for n-ZnO:In/p-Si hetero-jucntion; n=10.14, Is=3.2689.10-5 (A) for p-ZnO:(In, N)/n-Si, respectively. These results demonstrated the formation of a diode between n-type thin film and p-Si, as well as between p-type thin film and n-Si..