A credit scoring model based on the Myers–Briggs type indicator in online peer-to-peer lending

Woo, Hyunwoo; Sohn, So Young

doi:10.1186/s40854-022-00347-4

Cited by 10 publications

(3 citation statements)

References 79 publications

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“…P2PL has become very attractive in the postcovid-19 era because of its ease in providing loans. On the other hand, when many people need online loans and many people do not have the opportunity to obtain conventional bank loans, P2PL is to be a complement in providing access to loans (Nigmonov et al 2022;Tang, 2019;Woo and Sohn, 2022). P2PL also contributes to productive financing sectors.…”

Section: Methodsmentioning

confidence: 99%

Performance Mapping Of Fintech Peer To Peer Lending (P2PL) in Indonesia

Situmorang

Siregar

Zulbainarni

et al. 2023

jabm

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The development of Peer-to-Peer Lending (P2PL) fintech in Indonesia was growing fast. In the midst of this rapid growth, a volatile pattern shows the dynamics of the P2PL in terms of its performance. This study aims to map the performance of fintech P2PL. The data used are the total disbursement of loans and non-performing loans obtained from each company's website and aggregate data published by the Financial Services Authority (OJK). In this study, a website scraping from 102 fintech companies was obtained from each platform to obtain Non-Performing Loan (NPL) value and accumulated loan distribution. This study also uses the hierarchical clustering method to group each P2PL based on NPL and accumulated loan disbursement. Based on the hierarchical clustering analysis, three clusters distinguish the characteristics of grouping P2PL companies. In first cluster, there are 3 companies with high distribution and low NPL, while in the second cluster consists of 13 companies categorized as poor performance because they related to the low disbursement and high NPL value. In the third cluster there are 71 companies with moderate disbursement and NPL. Based on this mapping several things need to be improved, starting from developing a risk management and monitoring system, lending and operating supervision.

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Section: Methodsmentioning

confidence: 99%

Performance Mapping Of Fintech Peer To Peer Lending (P2PL) in Indonesia

Situmorang

Siregar

Zulbainarni

et al. 2023

jabm

View full text Add to dashboard Cite

show abstract

“…• economics and finance (Francis & Village, 2022;Ventre, Salvador, Martino, & Maturo, 2023;Woo & Sohn, 2022) • leadership (Turner & Elson, 2022;Zárate-Torres & Correa, 2023;Zhang, Yang, Lusha, Leung, & Lau, 2022) and • medical and pharmacy (Akhtar, Ashfaq, Khalid, & Baig, 2023;Flowers et al, 2023;Kodweis, Jasmin, Hall, & Havrda, 2023;Paul, 2023;Venurkar, Srivastava, Shukla, Acharya, Saha, & Deshpande, 2022).…”

Section: • Perceiving Informationmentioning

confidence: 99%

The Association of Personality Characteristics with Learning Strategy Preferences

Conti,

McNeil

2024

JEL

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The purpose of this study was to describe the association between the learning strategy preference of the learners as identified by Assessing The Learning Strategies of AdultS (ATLAS) and the individual personality traits as defined by the Myers-Briggs Type Indicator (MBTI). The sample was 553 adults in Canada and the United States. Two types of analyses were used to investigate the association between learning strategy preferences and personality traits. First, discriminant analysis explored the interaction of personality traits with the learning strategy preference. Second, analysis of variance measured the association of each personality trait with the learning-strategy-preference groups separately. The findings provided several explicit personality traits associated with each learning-strategy-preference group. These findings support the conclusion that a strong association exists between personality traits and learning-strategy-preference characteristics. Learning strategy preferences and personality traits complement each other. Each clarifies and enriches the other. As a result, teachers have two indicators that can help them personalize the teaching-learning environment for each student. Teachers can use the learning-strategy-group descriptions as guides for organizing each learner's instructional activities and plans and as a cognitive framework for uncovering and monitoring student behaviors and alerting teachers to potential learning difficulties for some students. Students can apply the descriptions of the learning-strategy-preference groups to facilitate self-assessment and metacognition. Theory can be enhanced by considering the two concepts of learning strategy preferences and personality traits coupled and by conducting quantitative and qualitative research to test and expand the generalizability of the learning-strategy-group descriptions. (Permission is granted to use Assessing The Learning Strategies of AdultS and the Personality Identity Estimator in practice and research. Links to printable copies and online completion are appended.)

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“…2.2.1 Loan description linguistic feature quantification. The length of the loan description reflects the importance attached to it by the borrower, and too short a description may make investors think that the borrower lacks confidence in his or her repayment ability [12]. The amount of information contained in the text is also related to the length of the text.…”

Section: Quantification Of Loan Description and Variable Designmentioning

confidence: 99%

How loan descriptions affect the likelihood that borrowers obtain loans in P2P networks? -An empirical analysis based on the "Renrendai" platform

Sun,

Wang,

Zhang

et al. 2023

PLoS ONE

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Information asymmetry is widespread in the P2P online lending market, creating an imbalance in the position of lenders and borrowers. This paper aims to expand the process of information exchange between lenders and borrowers by analyzing the link between soft information such as borrowers’ loan descriptions and lending outcomes. Based on the transaction data of the ‘Renrendai’ platform, this paper analyzed the linguistic features and extracted the content of loan descriptions using a latent Dirichlet allocation (LDA) theme model. To further explore the value of loan descriptions in predicting lending success, this paper conducts a prediction study based on a support vector machine model. It is found that: lenders focus on effective information in the loan descriptions, the linguistic complexity affects the transaction, with simple and direct statements being more favorable; the content for building a good personal image of the borrower will significantly contribute to the lending success. In the prediction study section, it is demonstrated that loan descriptions’ language feature indicators can improve prediction accuracy. This paper uncovers the importance of loan descriptions in online lending transactions, which has implications in assisting lenders’ investment judgments, as well as in platform information system improvements.

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A credit scoring model based on the Myers–Briggs type indicator in online peer-to-peer lending

Cited by 10 publications

References 79 publications

Performance Mapping Of Fintech Peer To Peer Lending (P2PL) in Indonesia

Performance Mapping Of Fintech Peer To Peer Lending (P2PL) in Indonesia

The Association of Personality Characteristics with Learning Strategy Preferences

How loan descriptions affect the likelihood that borrowers obtain loans in P2P networks? -An empirical analysis based on the "Renrendai" platform

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