원문정보
초록
영어
The global banking industry has undergone a wave of mergers over the last two decades and, as a result of which, mega banks have arisen in many countries around the world including Korea. Korean banks respond to this recent trend of bank consolidation by lowering the deposit interest rate and raising the loan interest rate (Park, 2004). Another major change had taken place even before the megabank trend. After the government allowed financial intermediation services by non-bank financial institutions in the late 1990s, commercial banks had started to reduce making loans to individual consumers since the monitoring costs are relatively higher than the interest income. As a result, banks have become more conservative in making loans to borrowers with low credit. In contrast to banks that strictly adhere to credit evaluation models, investors of online P2P (Peer-to-Peer) lending use much more flexible methods to judge borrowers’ qualifications for a loan, which include ‘hard information factors,’ ‘social interaction factors,’ and ‘soft information factors.’ Naturally, borrowers with low credit ratings use online P2P lending as one of the available funding sources. In this paper, we explore factors of success in online P2P lending repayment. In particular, this study examines how each of these three most common factors- ‘hard information factors’, ‘social interaction factors’, and ‘soft information factors’-- affect the loan repayment. This paper uses data from 542 loans from a leading crowd funding company in Korea from June 2008 through November 2010. We use a number of questions and answers, pre-poll results, and a number of attempts for making loans as the proxy variables of social interaction factors. Unlike foreign online P2P lending, Korea’s internet lending takes place through the social relationship between borrowers and investors, formed by communicating via the online bulletin board. We obtained the following empirical results. Firstly, borrowers’ personal bankruptcy experience has a significantly positive impact on the success of loan repayment. This is because borrowers with experience in personal bankruptcy have an incentive to improve their credit ratings through sincere loan repayment because a typical online P2P lending system uses a savings bank account as an escrow account. Therefore, they can improve their credit ratings for loans from commercial or savings banks by maintaining good credit standing online. These results imply that people with poor credit can use online P2P lending not only for raising money but also for improving their credit quality. Secondly, the purposes of loans for housing, living, and medical expenses have a significantly positive relationship with the success of loan repayment. This means that if they get loans for essential living expenses, they would exert greater efforts to repay the loans. Thirdly, debtors, who actively share personal information and interact with lenders online through P2P auction site message boards, are more likely to repay their loans than borrowers who have less social interaction. It appears that lenders can get more access to borrowers’ personal information and thus effectively monitor their loans. Borrowers can also establish trust with lenders and make a stronger effort to repay loans. The implications of our analysis are summarized as follows: Firstly, although earlier studies on personal loans have focused on personal bankruptcy decisions, this paper examines the effects of individual peculiarities on loan repayment. In particular, we find that bankrupt borrowers make greater efforts to repay the loans through online P2P lending. This is because if borrowers repay their loans in time, they can improve their overall credit ratings for loans from commercial banks in the future. Therefore, policy makers can consider online P2P lending as one of policy tools to encourage financial consumers with low credits for credit recovery. Secondly, we show that borrowers who raise money for essential living expenses make stronger efforts to repay their loans. Thirdly, we find that close interaction between potential borrowers and lenders is an important success factor in loan repayment. We conclude that such interaction can help reduce the information asymmetry, establish trust between borrowers and lenders, and raise the possibility of loan repayment. Our paper also has an important policy implication for consumer banking and small and medium enterprise finance. Domestic online P2P lending intermediaries work in partnership with savings banks and use their bank accounts as escrow accounts. However, due to the recent insolvencies of a number of large project finance operations, many savings banks are unable to play the roles of online P2P lending firms’ escrow accounts. For example, ‘Pop Funding’, one of the biggest online P2P lending intermediaries in Korea, had been in partnership with ‘Jeil Savings Bank’. But this bank was forced to shut down the operation because of its insolvency. Therefore, in order to firmly establish online P2P lending as a way to provide financing and credit enhancement for low credit borrowers, commercial banks should also play the role of providing escrow accounts for online P2P lending companies. In fact, European banks have recently been trying to enter the online P2P lending market more aggressively.
한국어
본 연구는 온라인 개인 간(Peer-to-Peer: P2P) 대출의 상환성공요인을 분석하였다. 이를 위해, 대출상환에 영향을 줄 수 있는 여러 요인들이 대출상환성공의 가능성과 어떠한 관계가 있는지 살펴보았다. 실증분석결과 첫째, 신용파산경험과 대출상환성공더미는 유의한 정(+)의 관계가 있는 것으로 나타났다. 이는 대출경매에 성공한 차입자 중 신용파산경험이 있는 차입자의 경우 대출금 상환을 통해 신용등급을 개선하여 기존 금융권으로의 복귀를 위해 노력하기 때문인 것으로 풀이된다. 둘째, 주거비용, 생활비용, 그리고 의료비용 등은 대출상환성공더미와 유의한 정(+)의 관계를 보였다. 즉, 생계를 위한 필수비용을 대출받은 차입자의 경우 대출금 상환에 더욱 적극적인 것으로 나타났다. 셋째, 활발한 사회교류활동을 한 차입자는 소극적으로 사회교류활동을 한 차입자에 비해 대출상환성공 가능성이 높았다. 이러한 결과는 사회적 교류활동이 차입자와 투자자 사이의 신뢰를 형성하고 정보교류를 가능하게 하여 대출상환에 긍정적으로 작용할 수 있음을 의미한다. 본 연구의 이러한 분석결과는 서민금융 활성화 측면에서 온라인 P2P대출이 신용등급이 매우 낮은 금융 소비자들의 필요 자금조달수단이 될 뿐만 아니라, 대출경매에 성공한 차입자 중 파산한 개인의 신용회복을 위한 수단으로도 적절히 활용될 수 있음을 시사한다.
목차
Abstract
Ⅰ. 서론
Ⅱ. 기존 연구 및 가설 설정
Ⅲ. 주요 변수정의 및 연구방법론
1. 주요 변수정의 및 예상부호
2. 표본 선정 및 연구방법
Ⅳ. 실증 분석
1. 주요변수의 기초통계량 및 상관관계 분석
2. t-검정 및 차이의 차이 검정(difference-in-differences)
3. 로지스틱 회귀분석(logistic regression analysis)
Ⅴ. 결론 및 시사점
참고문헌