Corrigendum to “A Trial of Student Self-Sponsored Peer-to-Peer Lending Based on Credit Evaluation Using Big Data Analysis”

Hou, Yujiao; Ma, Xiaofeng; Mei, Guang; Wang, Ning; Xu, Weisheng

doi:10.1155/2019/9095675

Cited by 1 publication

(2 citation statements)

References 1 publication

(1 reference statement)

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“…QN claimed that it uses big data and artificial intelligence, so each borrower will be assessed on his or her unique set of information and will be given a payment rate that is appropriate to his or her credit risk. Big data and artificial intelligence have been studied to contribute a beneficial effect on P2P Lending, such as reduced loan-default ratio, creating personalized lendings, and providing wider access to borrowers (Zhang et al , 2015; Hou et al , 2019). They also claimed that their process can eliminate subsidies from other borrowers’ systems through a one-size-fits-all model of pricing.…”

Section: Findings and Discussion Desk Researchmentioning

confidence: 99%

“…Also, using the latest technologies, P2P lending companies are innovating in their credit score mechanism and using it to better analyze customers’ applications on time (Hurley and Adebayo, 2017). The advent of this technological advancement in form of data-driven analysis, machine learning and big data contributes significantly to the development of credit scores as a whole, such as a portfolio optimization (Chi et al , 2019), reduced default ratio (Hou et al , 2019) and more extensive loan given to borrowers (Zhang et al , 2015) and more robust credit score model (Zhang et al , 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

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The bane of P2P lending: credit scoring governance on the ASEAN fintech triumvirate

Rosdini

Wahyuni²,

Sari³

2022

JSTPM

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Purpose This study aims to explore credit scoring regulations, governance, variables and methods used by peer-to-peer (P2P) lending platforms in key players of the Association of Southeast Asian Nations (ASEAN) region’s P2P, Indonesia, Malaysia and Singapore. Design/methodology/approach This study explores the P2P Lending characteristics of the three countries using qualitative literature review, interview, focus group discussion and desk research. Findings This study concludes that the credit scoring variables used by the countries’ companies are almost the same. Key drivers of the differences are countries’ regulations, management/business core value and credit scoring data processing methods. Practical implications Ultimately, this research provides a comprehensive view for investors, businesses and researchers on the topic of ASEAN credit scoring governance and will help them navigate the complexities and improve their awareness on the importance of credit scoring governance in P2P lending companies. Originality/value This research provides an in-depth perspective on how P2P lending companies, credit scoring governance and regulations in the biggest three countries in Southeast Asia.

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Section: Findings and Discussion Desk Researchmentioning

confidence: 99%