• Journal of Digital Convergence
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• v.10 no.9
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• pp.79-87
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• 2012

While the youth unemployment problem has come to extend over a long period of time, because of the promotion policy of youth inauguration of an enterprise, many youths have been considering the start-up to be an alternative of the working. But it is said that many youths who had started an enterprise through the start-up education and related programs were unsuccessful mostly and dashed to get a job or became the delinquent borrower. This article will examine the actual conditions of the youth unemployment and the present conditions of promotion policy of the youth start-up, analyse problems as a result of this examination, propose alternatives of policy to revitalize the youth start-up.

• 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.