Personal Credit Risk Evaluation Model of P2P Online Lending Based on AHP

Wu, Fengpei; Su, Xiang; Ock, Young-Seok; Wang, Zhiying

doi:10.3390/sym13010083

Cited by 12 publications

(9 citation statements)

References 15 publications

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“…Fintech technology can be used in reducing credit risk by making more reasonable credit assessments on credit default risk [22]. Personal credit evaluation is the most important thing in minimizing credit risk [23]. Other research reveals that fintech technology can be utilized in saving transaction costs, allocating credit resources, and better credit supervision [24].…”

Section: Technology Adoptionmentioning

confidence: 99%

The Effect of Fintech on Conventional Bank Performance and Bank Risk

Harmadi¹,

Untoro²,

Trinugroho³

et al. 2022

Advances in Economics, Business and Management Research

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Fintech in Indonesia is growing very rapidly, this is also very interesting for researchers. This study looks at the effect of Fintech on the performance and risk of conventional banks in Indonesia. The proxy for fintech in this study is P2P lending and the adoption of fintech technology by banks. The number of conventional banks in Indonesia is 107 banks, and this study took a sample of 81 conventional banks. This research period is for 5 years from 2017 to 2021. The results show that P2P lending and fintech adoption do not affect the performance or risk of conventional banks. However, what is interesting about the results of this study is that the interaction between each independent variable with the moderating variable of bank ownership structure shows the results of the influence of the independent variable on the dependent variable which is significant. We find that bank ownership structure is proven to strengthen the effect of P2P lending and fintech adoption on bank risk, while the effect on performance is not significant. We found that the presence of P2P lending will increase the risk of conventional banks. While the effect of fintech adoption shows a lowering effect on bank risk, especially those owned by cooperatives. Our findings support previous research results that the presence of P2P lending will increase bank risk, and the adoption of fintech will decrease risk.

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Section: Technology Adoptionmentioning

confidence: 99%

The Effect of Fintech on Conventional Bank Performance and Bank Risk

Harmadi¹,

Untoro²,

Trinugroho³

et al. 2022

Advances in Economics, Business and Management Research

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“…AHP is a crucial scientific decision-making model, which objectively quantifies the weight analysis process rather than subjective qualifying [15]. e core of AHP is a structured hierarchy process [16].…”

Section: Counting Influence Of Index Weight Determinationmentioning

confidence: 99%

Application of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies

Chen

Zhu

2021

Mathematical Problems in Engineering

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The purpose is to avert the systematic financial risks from the Internet financial bubble and improve the efficiency of legal service companies’ credit risk assessment ability. Firstly, this study analyzes the commonly used classification model, Support Vector Machine (SVM), and linear regression model, Logistic model, and then puts forward the integrated SVM-Logistic + Fuzzy Multicriteria Decision-Making (FMCDM) to evaluate and analyze the credit risk level of listed companies. In the proposed integrated model, the SVM model classifies the data sample from listed companies, and the Logistic model is used for regression analysis on the credit risk assessment. Based on the credit risk indexes and weight uncertain factors of sample companies, FMCDM based on fuzzy set is applied to obtain the evaluation indexes. Then, the Analytic Hierarchy Process (AHP) is used to obtain the weight of key indexes. Finally, the fit analysis is carried out according to the existing risk status of the sample company and the risk status results of the proposed integrated model. The results show that the integrated SVM-Logistic model is complementary and has high intensive evaluation. According to the fitness value obtained by FMCDM, the company's credit risk status can be accurately evaluated, and the intermediate threshold of corporate credit default risk measurement is 0.56152; if Fit is lower than the threshold, the company’s credit is low, and if Fit is higher than the threshold, the company’s credit is high. Therefore, the data mining technology based on integrated SVM-Logistic model + FMCDM has high precision and feasible application in the credit risk assessment from legal service companies. This study creates a new method model for legal service companies in the field of corporate credit risk assessment and can provide references and ideas for corporate credit risk assessment.

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“…With the increasing proportion of personal loans, personal credit risk has been rising, whether for bank loans or peer-to-peer (P2P) network loans. Thus, scholars have conducted an individual credit risk assessment to reduce personal credit risk [ 24 , 25 ]. Some scholars improved the BP neural network model and constructed an integrated IDGSO-BP model for micro- and small enterprises that can effectively improve the accuracy of credit risk assessments [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Assessing and Predicting Green Credit Risk in the Paper Industry

Zhao

Chen

2022

IJERPH

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The paper industry is closely related to forestry resources, which constitute an essential part of achieving sustainable development. Green credit can provide financial support to assist the paper industry in achieving carbon neutrality. To develop a method for performing green credit risk assessments in the paper industry, first, an initial index system was established on the basis of two dimensions: financial risk and socio-environmental risk. Then, the KMV model was applied to measure credit risk. The combined results of this model, along with the environmental penalties of an enterprise, formed the basis for the classification of green credit risk. Third, the Gini index was used to filter out, one by one, the indexes with the least influence among the factors, and then random forest iterations were performed until the prediction accuracy reached the optimum, thus establishing a green credit risk prediction model for the paper industry. The results show that the accuracy of the sample classification reached 93.75%, and the accuracy of the sample classification for high-risk enterprises reached 100%. The established index system offers good guidance for the assessment of green credit risk in the paper industry, in which the interest coverage ratio, current ratio, asset-liability ratio, and green emissions are the main factors affecting green credit risk.

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Personal Credit Risk Evaluation Model of P2P Online Lending Based on AHP

Cited by 12 publications

References 15 publications

The Effect of Fintech on Conventional Bank Performance and Bank Risk

The Effect of Fintech on Conventional Bank Performance and Bank Risk

Application of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies

Assessing and Predicting Green Credit Risk in the Paper Industry

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