The Analysis of Fintech Risks in China: Based on Fuzzy Models

Pi, Tianlei; Hu, Haoxuan; Lu, Jingyi; Chen, Xue

doi:10.3390/math10091395

Cited by 8 publications

(20 citation statements)

References 46 publications

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“…The application of this approach to internet financial risk warning and risk management research began as early as 2019 [ 15 ], gradually gaining momentum alongside technological development [ 11 , 19 , 25 ]. The primary focus of these studies lies in the selection of model methodologies [ 17 , 20 , 26 ] and the construction of risk systems [ 1 , 27 , 28 ]. However, to date, there has been no comprehensive review article or study systematically outlining the state of this emerging yet critical research field.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine learning in internet financial risk management: A systematic literature review

Tian,

Khatib

et al. 2024

PLoS ONE

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Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy. Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks.

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Section: Literature Reviewmentioning

confidence: 99%

Machine learning in internet financial risk management: A systematic literature review

Tian,

Khatib

et al. 2024

PLoS ONE

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“…The performance of Dombi AOs when associated with Hamy [57] and Heronian [17] mean can also give us productive results in MADM problems. Further, such AOs-based theories can be utilized in Fintech risks [58], in the evaluation of noise reduction devices [59], and in the evaluation of green supply chains [59,60].…”

Section: Discussionmentioning

confidence: 99%

An Approach for the Analysis of Energy Resource Selection Based on Attributes by Using Dombi T-Norm Based Aggregation Operators

et al. 2022

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Dombi t-norm (DTN) and t-conorm (TCN) are among the most effective triangular norms in fuzzy systems for aggregation purposes. The environment of interval-valued intuitionistic fuzzy (IVIF) set gives us precision in expressing uncertain information by using a membership grade (MG) and non-membership grade (NMG) in the form of closed subintervals of 0, 1. The goal of this paper is to introduce DTN-based aggregation operators (AOs) for IVIF numbers (IVIFNs) and study their performance in the evaluation of the worth of energy recourses to be opted in Pakistan to deal with the energy crises situation. We first introduced some DTN and TCN-based operations for IVIFNs and developed two new AOs known as IVIF Dombi weighted averaging (IVIFDWA) and IVIF Dombi weighted geometric (IVIFDWG) operators. The validity and fitness of the proposed operators are tested. A case study is presented where the energy resources of Pakistan are discussed and the problem of the selection of sustainable energy resources in the context of Pakistan is investigated. The sensitivity analysis of the proposed IVIFDWA and IVIFDWG operators is studied and a comparative analysis of the current work with previous studies is established.

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“…Just similar to insurance, moral hazard often is accompanied with finance. For instance, Fintech Risks in China and found that the technology risk, moral hazard and legal risk are the dominant factors affecting the Fintech risk (Tianlei Pi et al, 2022). Simultaneously, Xueer Chen and Chao Wang(2021) demonstrated that the application of Fin Tech in China's banking industry resulted in less information asymmetry.…”

Section: Moral Hazardmentioning

confidence: 99%

Reexamine the Role of the Financial Institution Management During Card Debt Crisis or the COVID-19

2022

JABE

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Due to the increasing popularity of financial technology and the lifting of financial regulations, various financial institutions have become increasingly competitive and actively expand their consumer finance business. Changes in generational consumption behavior have led to excessive credit expansion, excessive debt or bad credit records. All of these result in the emergence of adverse selection and moral hazard problems of information asymmetry, and finally cause the card debt crisis in 2005. This article focuses on variables such as the number of cards in circulation, retail sales volume, revolving balance, and overdue ratios of credit cards in public and private banks, and examine whether the information asymmetry in the credit card market has been improved ,with the financial institution management. Furthermore, due to the COVID-19 ,exploring whether the information asymmetry has been worsened or improved deserves the attention of the financial authority again. The results reveal that continuous financial institution management is very important and effective during the card debt period or the pandemic.

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The Analysis of Fintech Risks in China: Based on Fuzzy Models

Cited by 8 publications

References 46 publications

Machine learning in internet financial risk management: A systematic literature review

Machine learning in internet financial risk management: A systematic literature review

An Approach for the Analysis of Energy Resource Selection Based on Attributes by Using Dombi T-Norm Based Aggregation Operators

Reexamine the Role of the Financial Institution Management During Card Debt Crisis or the COVID-19

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