Exploration of Financial Market Credit Scoring and Risk Management and Prediction Using Deep Learning and Bionic Algorithm

Shu, Hong Ling

doi:10.4018/jgim.293286

Cited by 32 publications

(15 citation statements)

References 40 publications

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“…This paper combined econometric theory and text mining technology to achieve the risk knowledge acquisition of the P2P lending platform. Given the rapid development of advanced technology such as machine learning (Cheng et al, 2021;Srivastava and Eachempati, 2021), deep learning (Du and Shu, 2022;Hou et al, 2022;Wu et al, 2022), and the internet of Things (Almomani et al, 2021;Huang et al, 2021;Peng et al, 2021), future research should employ the latest technology to obtain more accurate results.…”

Section: Discussionmentioning

confidence: 99%

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Peer-to-peer lending platform risk analysis: an early warning model based on multi-dimensional information

Xia

Wang

Zhang

et al. 2022

JRF

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PurposeThe paper focuses on the variables that help analyze peer-to-peer (P2P) lending platforms. It explores the characteristic factors of identifying problematic platforms, and designs a P2P platform risk early warning model.Design/methodology/approachWith the help of web crawler software, this paper crawls the information of 1427 P2P platforms from the two largest third-party lending information platforms (i.e. P2Peye and WDZJ) in China. SPSS 22.0 was mainly used for basic descriptive statistical analysis, reliability and validity analysis, and regression analysis of the data. MPLUS 7.0 was used for confirmatory factor analysis and structural equation models analysis.FindingsBased on the multi-dimensional information, this paper performs text mining to develop an investor sentiment index. This study shows that the characteristics of the platform (i.e. basic features, capital security, operations management, and social network) have a significant impact on identifying problematic platforms.Research limitations/implicationsThere are some limitations to this research. In the process of model construction, some external factors may be ignored, such as government policies. Future research will need to consider the impact of policy and other factors more comprehensively on P2P lending platform risk identification.Practical implicationsThis study proposes an effective method for investors and regulators to identify the risk factors of P2P lending platforms. The research findings provide valuable insights for promoting government participation in platform management as well as a healthy development of the P2P lending industry.Originality/valueThe paper addresses the factors that influence platform risks to help analyze P2P lending platforms. Prior research has not explored how to identify problematic P2P lending platforms in-depth and is limited by only focusing on either soft information or hard information. It identifies the characteristic factors of identifying problematic platforms and designs a P2P platform risk early warning model.

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Section: Discussionmentioning

confidence: 99%

“…Given the rapid development of advanced technology such as machine learning (Cheng et al. , 2021; Srivastava and Eachempati, 2021), deep learning (Du and Shu, 2022; Hou et al. , 2022; Wu et al.…”

Section: Discussionmentioning

confidence: 99%

Peer-to-peer lending platform risk analysis: an early warning model based on multi-dimensional information

Xia

Wang

Zhang

et al. 2022

JRF

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“…Kapitonov used statistical analysis, mathematical modeling and standard deep learning model is proposed. Finally, a financial credit risk management system using integrated deep learning model is proposed (Du and Shu, 2022).…”

Section: Related Workmentioning

confidence: 99%

Early warning of enterprise financial risk based on improved BP neural network model in low-carbon economy

Wan

Yu²

2023

Front. Energy Res.

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The concept of low-carbon economic development has led to changes in the business environment and financial environment of enterprises, leading to increased financial risks faced by enterprises. How to help enterprises better warn, prevent and control financial risks from the perspective of low-carbon economy has become a hot issue worth studying. Based on this, this paper is based on the perspective of low carbon economy, on the basis of analyzing the financing risk, investment risk, capital operation risk and growth risk faced by enterprises under the requirements of low carbon economy development. A set of financial risk management framework with clear hierarchy and strict vertical logic has been constructed. Ten financial early-warning indicators are constructed from four aspects. The risk prediction model of the indicator system is established using the research method of BPNN (Back Propagation Neural Network). The model is trained and simulated through the MATLAB neural network toolbox. After 10 indicators passed Bartlett’s correlation test, the BPNN financial early warning model was programmed using MATLAB software. The accuracy rate was 84.3%. The neural network training results show that when the layer node is 8, the best correct recognition rate can be obtained. Incorporate “low carbon” into the financial risk early warning indicator system that meets the requirements of low carbon economic development in the design of enterprise financial risk early warning indicators. This paper is expected to provide reference and reference for low-carbon economy enterprises to deal with financial risks under the new situation.

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“…KM processes can only better realize their value if their security and visibility can be improved by blockchain technology adaptation (Li et al, 2021). Many emerging technologies, such as the internet of Things (Almomani et al, 2021;Huang et al, 2021;Peng et al, 2021), machine learning (Cheng et al, 2021;Srivastava and Eachempati, 2021;Trappey et al, 2022), deep learning (Du and Shu, 2022;Hou et al, 2021;Wu et al, 2021) and big data analytics (Bag et al, 2021;Gholami et al, 2021;Xie et al, 2022), are driving the creation of new business models to streamline KM processes. Among them, blockchain technology-an advanced database mechanism for firms that allows information to be shared transparently across a network of firms-guarantees data reliability in knowledge learning and application among SC stakeholders (Ali et al, 2020;Buthelezi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Blockchain technology adaptation and organizational inertia: moderating role between knowledge management processes and supply chain resilience

Zhou

Zhang

et al. 2023

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PurposeSupply chain resilience and knowledge management (KM) processes have received increasing attention from researchers and practitioners. Nevertheless, previous studies often treat the two streams of literature independently. Drawing on the knowledge-based theory, this study aims to reconcile these two different streams of literature and examine how and when KM processes influence supply chain resilience.Design/methodology/approachThis research develops a conceptual model to test a sample of data from 203 Chinese manufacturing firms using a structural equation modeling method. Specifically, the current study empirically examines how KM processes affect different forms of supply chain resilience (supply chain readiness, responsiveness and recovery) and examines the moderating effect of blockchain technology adaptation and organizational inertia on the relationship between KM processes and supply chain resilience.FindingsThe findings show that KM processes positively affect three dimensions of supply chain resilience, i.e., supply chain readiness, responsiveness and recovery. Besides, the study reveals that blockchain technology adoption positively moderates the relationships between KM processes and supply chain resilience, whereas organizational inertia negatively moderates these above relationships.Originality/valueThis research linked the two research areas of supply chain resilience and KM processes, further bridging the gap in the research exploration of KM in the supply chain field. Next, this study contributes to supply chain resilience research by investigating how KM systems positively impact supply chain readiness, responsiveness and recovery. In addition, this study found a moderating effect of blockchain technology adaption and organizational inertia on the relationship between KM processes and supply chain resilience. These findings provide a reference for Chinese manufacturing firms to strengthen supply chain resilience, achieve secure supply chain operations and gain a competitive advantage in the supply chain. This studys’findings advance the understanding of supply chain resilience and provide practical implications for supply chain managers.

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Exploration of Financial Market Credit Scoring and Risk Management and Prediction Using Deep Learning and Bionic Algorithm

Cited by 32 publications

References 40 publications

Peer-to-peer lending platform risk analysis: an early warning model based on multi-dimensional information

Peer-to-peer lending platform risk analysis: an early warning model based on multi-dimensional information

Early warning of enterprise financial risk based on improved BP neural network model in low-carbon economy

Blockchain technology adaptation and organizational inertia: moderating role between knowledge management processes and supply chain resilience

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