Mining Semantic Soft Factors for Credit Risk Evaluation in Peer-to-Peer Lending

Zhao, Wang; Jiang, Cuiqing; Zhao, Huimin; Ding, Yong

doi:10.1080/07421222.2019.1705513

Cited by 49 publications

(24 citation statements)

References 36 publications

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“…These factors are a complicated process, business plan bad, as well as high credit costs. For lenders, access inside information loan-based crowd funding can help them to reduce misinformation, so that it can help them make decisions on a loan application [33]. MSMEs are a major factor in economic development.…”

Section: Resultsmentioning

confidence: 99%

Financing Analysis of Loan-Based Crowd Funding on Medium Small Micro Enterprises

Dewanti¹,

Septyaningtyas²,

Yuliansyah³

et al. 2021

Proceedings of the 2nd Annual Conference on Social Science and Humanities (ANCOSH 2020)

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This study aims to determine the results of analysis Loan-Based Crowd funding on MSMEs in several countries on helping MSMEs obtain capital loans without a series of difficulties in administration and regulations. So, it can be used as a consideration for the government in making policies that can be applied in Indonesia. In this study, references from journal articles analyze to solve the problem. In this study, using references from journal articles analyzed to solve the problem. This research shows that loan-based crowd funding can be of positive value for business actors with the ease of making loans to maintain and develop the business.

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Section: Resultsmentioning

confidence: 99%

Financing Analysis of Loan-Based Crowd Funding on Medium Small Micro Enterprises

Dewanti¹,

Septyaningtyas²,

Yuliansyah³

et al. 2021

Proceedings of the 2nd Annual Conference on Social Science and Humanities (ANCOSH 2020)

View full text Add to dashboard Cite

show abstract

“…It is also possible to crowdsource more labels for the training data based on a small initial dataset coded by the researcher or to produce a human coded dataset that the algorithm's results are compared to. However, crowdsourcing annotation has challenges regarding label noise, intercoder reliability and allocation [51,57].…”

Section: Noisy Datamentioning

confidence: 99%

Qualitative Big Data’s Challenges and Solutions: An Organizing Review

Suvivuo¹

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Digitalization of everyday lives has tremendously increased the amount of digital (trace) data of people's behaviour available for researchers. However, traditional qualitative research methods struggle with the width and breadth of the data. This paper reviewed 61 recent studies that had utilized qualitative big data for the practical challenges they had encountered and how they were addressed. While quantitative and qualitative big data share many common issues, the review points at that lack of qualitative methods and dataset reduction required by algorithms in big data research decreases the richness of the qualitative data. Locating relevant data and reducing noise are further challenges. Currently, these challenges can be only partially addressed with a combination of human and computer pattern recognition and crowdsourcing. The review describes many "tricks of the trade" but abduction research and pragmatist philosophy seem promising starting places for a more pervasive framework.

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“…In fact, nowadays, the relationship between the expected behaviour of enterprises and their past social relations as well as their past behaviour rules is far more important than that of their financial information. For instance, Wang et al proved that besides conventional hard information, soft information like behaviour information also enters into the lending decision process [35]. erefore, we add the behaviour information to supplement the content of the credit risk indicator system.…”

Section: Complexitymentioning

confidence: 99%

Credit Risk Assessment for Small and Microsized Enterprises Using Kernel Feature Selection-Based Multiple Criteria Linear Optimization Classifier: Evidence from China

Wang

2020

Complexity

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Credit risk assessment has gained increasing marked attention in the recent years by researchers, financial institutions, and banks, especially for small and microsized enterprises. Evidence shows that the core of small and microsized enterprises’ credit risk assessment is to construct a scientific credit risk indicator system, and the key is to establish an effective credit risk prediction model. Therefore, we analyze the factors that influence the credit risk of Chinese small and microsized enterprises and then construct a comprehensive credit risk indicator system by adding behaviour information, supervision information, and policy information. Furthermore, we improve the multiple criteria linear optimization classifier (MCLOC) by introducing the one-norm kernel feature selection and thereby establish the kernel feature selection-based multiple criteria linear optimization classifier (KFS-MCLOC). As for experiments, we use real business data from a Chinese commercial bank to test the performance of these models. The results show that (1) the proposed KFS-MCLOC has greater advantages in predictive accuracy, interpretability, and stability than other models; (2) the KFS-MCLOC selects 10 features from 53 original features and gives selected features their weight automatically; (3) the features selected by the KFS-MCLOC are further verified and compared by the features selected by the logistic regression model with stepwise parameter, and the indicators of “quick ratio; net operating cash flow; enterprises’ abnormal times of water, electricity, and tax fee; overdue days of enterprises’ loans; and mortgage and pledge status” are proved to be the most influencing credit risk factors.

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Mining Semantic Soft Factors for Credit Risk Evaluation in Peer-to-Peer Lending

Cited by 49 publications

References 36 publications

Financing Analysis of Loan-Based Crowd Funding on Medium Small Micro Enterprises

Financing Analysis of Loan-Based Crowd Funding on Medium Small Micro Enterprises

Qualitative Big Data’s Challenges and Solutions: An Organizing Review

Credit Risk Assessment for Small and Microsized Enterprises Using Kernel Feature Selection-Based Multiple Criteria Linear Optimization Classifier: Evidence from China

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