Application of innovative risk early warning mode under big data technology in Internet credit financial risk assessment

Du, Guansan; Liu, Zixian; Lu, Haifeng

doi:10.1016/j.cam.2020.113260

Cited by 112 publications

(67 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Then, a BPNN-based risk early warning model of Internet credit finance is established, which is proved to show higher accuracy in predicting the default rate of Internet credit personnel and identifying and classifying their credit risks. Such results are similar to Du et al (2021). It proves that compared with traditional credit assessment, big data technology can not only reduce the workload, rationally allocate resources, and improve work efficiency, but it can also reduce human errors in traditional methods.…”

Section: Discussionsupporting

confidence: 69%

“…Kou et al (2019) applied the machine learning methods in the system risk analysis of the financial sector [8]. Du et al (2021) proposed an innovative risk early warning model based on big data technology and applied it to the evaluation of Internet credit financial risk [9]. The development of big data technology and machine learning has greatly improved the efficiency of data processing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Innovative Risk Early Warning Model Based on Internet of Things Under Big Data Technology

Liu

2021

IEEE Access

View full text Add to dashboard Cite

An innovative financial risk early warning model based on the Internet of Things (IoT) big data technology is proposed to maintain the long-term stable development of Internet finance. The Internet credit finance is introduced, a financial risk identification method based on the big data technology is proposed, and a risk assessment method for Internet finance based on back propagation neural network (BPNN) is put forwarded in this study. The actual Internet financial data is selected for verification and analysis. The results reveal that the identification and prediction model of Internet credit finance risk based on big data technology can realize risk analysis of online credit in the Internet credit finance, and can give corresponding credit ratings to new customers on credit platforms. The training error is the lowest when the number of hidden layer nodes is 14; and the training error is the smallest when the learning rate is 0.06; Based on the BPNN, it can accurately assess the financial risks of the Internet credit platform, and the accuracy of its risk level prediction has reached 100%. This study can provide a theoretical basis for the application of IoT big data technology and neural network models in the financial field, and also give an important reference for the risk management of Internet finance.

show abstract

Section: Discussionsupporting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Innovative Risk Early Warning Model Based on Internet of Things Under Big Data Technology

Liu

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Decision tree algorithm is one of the most important methods in data risk analysis. Decision tree divides the whole data into treelike state graph according to the relationship among hierarchy, state, and data and completes data depth analysis [10]. e pivot points within the decision tree structure are the key points connecting all data.…”

Section: Risk Assessment Model Of Network Finance Technology Data Based On Portfolio Empowermentmentioning

confidence: 99%

Research on Risk Assessment of Science and Technology Data in Network Finance Based on Portfolio Empowerment

Zhao

2021

Security and Communication Networks

View full text Add to dashboard Cite

At present, the proposed network finance technology data risk assessment time is too long, leading to low accuracy. In order to solve the above problems, this paper puts forward the research on the risk assessment of network financial S&T data based on portfolio weighting, determines the risk index of network financial S&T data, calculates the weight of risk data in network S&T data, searches the risk data characteristic quantity in networks according to network S&T risk index, and completes the extraction of risk data. According to the risk data characteristics of network finance, a decision tree is constructed, the data entropy involved in the decision tree is calculated, the types of risk data characteristics are induced, the nodes of the decision tree are created, and the status of risk data of network finance is obtained. The state of risk data is brought into the definition of Bayesian network probability, and the risk degree of risk data is analyzed to improve the precision of risk data analysis. The experimental results show that the risk assessment of network financial S&T data based on portfolio weighting can effectively shorten the assessment time and improve the accuracy.

show abstract

“…The literature on EWS is more the domain of practitioners than academics. Given the higher degree of freedom in the model design, most of the recent EWS proposed in the literature [ 56 , 57 ] are based on machine learning techniques.…”

Section: Methodsmentioning

confidence: 99%

Predicting corporate credit risk: Network contagion via trade credit

et al. 2021

View full text Add to dashboard Cite

Trade credit is a payment extension granted by a selling firm to its customer. Companies typically respond to late payments from their customers by delaying payments to suppliers, thus generating a ripple through the transaction network. Therefore, trade credit is as a potential vehicle of propagation of losses in case of default events. The goal of this work is to leverage information on the trade credit among connected firms to predict imminent defaults of firms. We use a unique dataset of client firms of a major Italian bank to investigate firm bankruptcy between October 2016 to March 2018. We develop a model to capture network spillover effects originating from the supply chain on the probability of default of each firm via a sequential approach: the output of a first model component on single firm features is used in a subsequent model which captures network spillovers. While the first component is the standard econometrics way to predict such dynamics, the network module represents an innovative way to look into the effect of trade credit on default probability. This module looks at the transaction network of the firm, as inferred from the payments transiting via the bank, in order to identify the trade partners of the firm. By using several features extracted from the network of transactions, this model is able to predict a large fraction of the defaults, thus showing the value hidden in the network information. Finally, we merge firm and network features with a machine learning model to create a ‘hybrid’ model, which improves the recall for the task by almost 20 percentage points over the baseline.

show abstract

Application of innovative risk early warning mode under big data technology in Internet credit financial risk assessment

Cited by 112 publications

References 16 publications

Innovative Risk Early Warning Model Based on Internet of Things Under Big Data Technology

Innovative Risk Early Warning Model Based on Internet of Things Under Big Data Technology

Research on Risk Assessment of Science and Technology Data in Network Finance Based on Portfolio Empowerment

Predicting corporate credit risk: Network contagion via trade credit

Contact Info

Product

Resources

About