Innovative Risk Early Warning Model under Data Mining Approach in Risk Assessment of Internet Credit Finance

Lin, Min Huei

doi:10.1007/s10614-021-10180-z

Cited by 24 publications

(12 citation statements)

References 25 publications

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“…The number of nodes in the implicit layer of the model is complicated to determine and is generally set by empirical values which is based on the sample size and the number of variables [ 32 ]. Therefore, the number of nodes in the implicit layer is set as follows:

where m is the number of input nodes, n is the number of output nodes, and a is any constant between 1 and 10.…”

Section: Algorithm Designmentioning

confidence: 99%

Study on the Practice of Enterprise Financial Management System under the Epidemic Norm Based on Artificial Neural Network

2022

BioMed Research International

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The sudden arrival of the new crown epidemic has had a significant and long-lasting impact on the division’s economic environment as well as the production and operation activities of businesses. As far as the financial management is concerned, opportunities and difficulties are faced by enterprises of all types. With reference to the available research data, enterprises have an important contribution to GDP and jobs, but they still face a series of difficulties and challenges in their development in the context of the normalization of the epidemic. By analyzing the impact of the new crown pneumonia epidemic on the financial management work of enterprises, this paper proposes an artificial neural network-based enterprise financial forecasting and early warning method to provide an effective method for enterprise financial management. For the time-series characteristics of enterprise finances, a prediction model based on long- and short-term memory networks is developed which acknowledges the necessity of combining the temporal dimension with the spatial dimension for forecasting. This model incorporates time qualities into the data to the existing forecasting model. It also considers both working and nonworking day data and thoroughly considers the factors influencing corporate finance. Then, using BP neural network for financial risk prediction, nonfinancial index factors should be added to the financial early warning model thus eliminating the limitations of the financial early warning model. At the same time, the accuracy of the prediction can be improved which is more suitable for enterprises to apply in practice. The experimental results demonstrate that the financial prediction model built by multilayer feed forward neural networks and recurrent neural networks based on error back propagation training is inferior to the prediction model built by long- and short-term memory network. Regardless of the degree of fitting or prediction accuracy, the BP neural network model outperforms the conventional model for enterprise financial warning. Under the normalization of the pandemic, the combined use of both can offer an efficient technique for enterprise management.

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where m is the number of input nodes, n is the number of output nodes, and a is any constant between 1 and 10.…”

Section: Algorithm Designmentioning

confidence: 99%

Study on the Practice of Enterprise Financial Management System under the Epidemic Norm Based on Artificial Neural Network

2022

BioMed Research International

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“…With the development of mobile communication technology, the rise of the Internet of Things, and the advent of the era of industry 4.0, the sensor industry, agriculture, communications, space exploration, and other fields play an important role. At present, the domestic high-end sensor market is still occupied by foreign manufacturers, and China's sensor industry needs to increase investment to meet the needs of the domestic high-end market as soon as possible [1]. Sensors can be divided into analog sensors and digital sensors according to the nature of their output quantities.…”

Section: Introductionmentioning

confidence: 99%

Financial Early Warning Model for Listed Companies Based on the Smart Sensor Data Network

Wang

2022

Journal of Sensors

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This paper uses intelligent sensors to build a data network, collects information about listed enterprises in all aspects, performs frequency statistics and semantic analysis based on the financial domain lexicon on information related to listed enterprises, and introduces big data variables into a nonlinear support vector machine early warning model of enterprises combined with financial indicators. This paper introduces online reviews as big data indicators based on financial early warning theories and methods. Suitable financial indicators and big data indicators are selected for the financial early warning model to filter the available indicators. The prediction results of only financial indicators are compared with the prediction results of incorporating big data indicators. Quantify the comment information related to enterprises on the platform through sentiment classification and statistics on the number of comment information posted. The cost-sensitive support vector machine is used as the base classifier of the improved AdaBoost algorithm to build a dynamic imbalance warning model. To address the problems of large complexity and computation of unbalanced big data classification, high reliance on a priori knowledge, and classification performance to be improved, the classification and detection method of the intelligent sensor data network is proposed. By introducing migration learning, the problem of knowledge acquisition and training efficiency of high-dimensional complex data feature extraction in a big data environment is effectively solved, and the network performance is optimized by a conjugate gradient descent algorithm. Through simulation experiments, the prediction accuracy of the model for positive class samples is significantly improved after the nonequilibrium processing, the recall rate of the network model reaches 84.15%, and the prediction accuracy of the network model under different time steps reaches more than 90%. The experiment proves that the model can send out financial alert information more quickly and efficiently and accurately when a financial crisis may occur, relative to the traditional financial forecasting methods.

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“…e MIF risk levels have been classified based on the "3sigma" rule [26]. e statistical term used to determine the Degree of Deviation (DoD) in data samples is the "3sigma" criterion.…”

Section: Resultsmentioning

confidence: 99%

Construction of Mobile Internet Financial Risk Cautioning Framework Based on BP Neural Network

Zang

2022

Mobile Information Systems

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With the emergence of the 21st-century global economy, the international financial system faces economic risks. A competitive cautioning model for financial management is required to mitigate risks and losses in the financial sector. The financial losses of the banking industry have been categorized and analyzed using the Internet of Things (IoT) and big data technologies to minimize the economic risk of commercial banks in mobile internet finance (MIF). This article proposes a new financial risk cautioning framework (FRCF) based on the IoT, big data, and back propagation-neural network (BP-NN) to ensure steady growth of MIF in the long term. In this article, a big data technology-based approach for data recognition and mining has been suggested. A BP-NN-based method for risk identification and assessment in MIF is also presented. The BP-NN technique calculates each neural network (NN) layer’s node count, transfer functions, learning rate, and other characteristics. The proposed FRCF has been developed through the proper construction, analysis, and testing of many information samples. A conceptual understanding of the use of IoT, big data, and artificial intelligence (AI) technologies through NN models in the financial industry has been described in the article. The proposed FRCF can predict the MIF risks associated with the MIF lending infrastructure with a 98.2% accuracy.

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Innovative Risk Early Warning Model under Data Mining Approach in Risk Assessment of Internet Credit Finance

Cited by 24 publications

References 25 publications

Study on the Practice of Enterprise Financial Management System under the Epidemic Norm Based on Artificial Neural Network

Study on the Practice of Enterprise Financial Management System under the Epidemic Norm Based on Artificial Neural Network

Financial Early Warning Model for Listed Companies Based on the Smart Sensor Data Network

Construction of Mobile Internet Financial Risk Cautioning Framework Based on BP Neural Network

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