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(searched for: doi:10.1016/j.najef.2021.101383)
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Zixuan Zhang, Xiaojun Jia, Shan Chen, , Fang Wang
Published: 21 September 2022
Computational Intelligence and Neuroscience, Volume 2022, pp 1-10; https://doi.org/10.1155/2022/1465394

Abstract:
P2P lending is an important part of Internet finance, which is popular among users because of its efficiency, low cost, wide range, and ease of operation. The problem of predicting loan defaults is affected by many factors, such as the linear and nonlinear nature of the data itself and time dependence and multiple external factors, which have not been well captured in the previous work. In this paper, we propose a multiattention mechanism to capture the different effects of various time slices and various external factors on the results, introduce ARIMA and LSTM to capture the linear and nonlinear characteristics of the lending data respectively, and establish a Time Series Multiattention Prediction Model (MAT-ALSTM) based on LSTM and ARIMA. This paper uses the Lending Club dataset from the United States to prove that our model is superior to ANN, SVM, LSTM, GRU, and ARIMA models in the prediction effect of MAE, RMSE, and DA.
Published: 13 July 2022
Operations Management Research pp 1-16; https://doi.org/10.1007/s12063-022-00293-5

Abstract:
A sound credit assessment mechanism has been explored for many years and is the key to internet finance development, and scholars divide credit assessment mechanisms into linear assessment and nonlinear assessment. The purpose is to explore the role of two important data analytics models including machine learning and deep learning in internet credit risk assessment and improve the accuracy of financial prediction. First, the problems in the current internet financial risk assessment are understood, and data of MSE (Micro small Enterprises) are chosen for analysis. Then, a feature extraction method based on machine learning is proposed to solve data redundancy and interference in enterprise credit risk assessment. Finally, to solve the data imbalance problem in the credit risk assessment system, a credit risk assessment system based on the deep learning DL algorithm is introduced, and the proposed credit risk assessment system is verified through a fusion algorithm in different models with specific enterprise data. The results show that the credit risk assessment model based on the machine learning algorithm optimizes the standard algorithm through the global optimal solution. The credit risk assessment model based on deep learning can effectively solve imbalanced data. The algorithm generalization is improved through layer-by-layer learning. Comparison analysis shows that the accuracy of the proposed fusion algorithm is 25% higher than that of the latest CNN (Convolutional Neural Network) algorithm. The results can provide a new research idea for the assessment of internet financial risk, which has important reference value for preventing financial systemic risk.
Zhishan Xie
Published: 1 June 2022
Mobile Information Systems, Volume 2022, pp 1-11; https://doi.org/10.1155/2022/3495504

Abstract:
The traditional identification methods of transaction risk characteristics mostly use the amount of profit and cost to complete the risk assessment. The conflict between the two will lead to low stability. Therefore, the identification method of transaction risk characteristics of transnational financial derivatives is proposed. The fuzzy support vector machine method is used to identify the risk characteristics of cross-border financial derivatives transactions and compensate the errors in the identification process. The possibility index, severity index, and financial risk index of cross-border financial derivatives transaction risk are calculated, and the financial risks are screened and ranked. The distance from risk element to positive ideal solution and negative ideal solution is defined, and the joint identification model of transaction risk of transnational financial derivatives is constructed to realize the identification of transaction risk characteristics. The experimental results show that the designed method has good recognition effect and high recognition performance.
Liang Chen, Rui Ma
Published: 9 May 2022
Mathematical Problems in Engineering, Volume 2022, pp 1-9; https://doi.org/10.1155/2022/4844856

Abstract:
To improve the ability of market to avoid and prevent credit risk and strengthen the awareness of market risk early warning, SMOTE is used to process the unbalanced sample, and fruit fly optimization algorithm (FOA) is utilized to optimize the parameters of support vector machine (SVM), and thus an improved SVM market risk early warning model is proposed. The simulation results show that the proposed model has excellent stability and generalization ability, and it can predict market credit risk accurately. Compared with the prediction model based on FOA-SMOTE-BP and FOA-SMOTE-Logit, the proposed model performs better on the indicators of G value, F value, and AUC value, which provides a reference for market credit risk prediction.
Huang Zhang, Yonghui Luo
Journal of Interconnection Networks, Volume 22; https://doi.org/10.1142/s0219265921450195

Abstract:
In this paper, an enterprise financial risk indicator system is established to warn about the financial risk of enterprises. First, the related knowledge of financial risk and its measurement is introduced. Next, the financial risk indicator system of small- and medium-sized enterprises (SMEs) is established based on back propagation neural network (BPNN). The rough set theory is adopted to simplify the indicator. Finally, the BPNN model is used to predict the financial situation of SMEs. The results show that in the 490th iteration, the performance of the BPNN-based financial risk early warning system for SMEs can reach the optimal and meet the accuracy requirements of initialization. The error of the enterprise financial risk early warning model converges to the target error, so the calculation result is credible. The actual output after training is close to the expected output. By judging the actual output value, it can be known that the financial risk status of SMEs in 2016, 2017 and 2018 is of low alarm. This exploration has a certain preventive effect on the financial risk of enterprises and provides a basis for the rapid development of enterprises.
Published: 2 May 2022
Mobile Information Systems, Volume 2022, pp 1-12; https://doi.org/10.1155/2022/4398602

Abstract:
Based on DL theory, this paper discusses and studies the early warning of enterprise financial risks in detail. And put forward a new enterprise financial risk early-warning model. The purpose is to enable enterprises to better analyze the changing trend of financial data, make correct decisions by managers and investors of enterprises, and promote the stable development of national economy and enterprises. This model is based on the early-warning theory of enterprises, based on the financial statements, business plans, and other relevant accounting information of enterprises, using accounting, finance, and marketing theories, adopting the methods of ratio analysis, comparative analysis, factor analysis, etc., to warn the financial risks of enterprises. This paper uses a lot of data to train the parameters of the DL financial early-warning model and then verifies the established financial early-warning model. In order to verify the reliability of this model, this model is compared with other two financial early-warning models. The results show that the prediction accuracy of this model is as high as 94%, which is 8~15% higher than that of other models. In this paper, the DL method has been applied to financial risk early warning and achieved good results. It has certain theoretical and practical significance in the field of enterprise financial early warning.
O. V. Borisova, M. P. Lazarev, S. Y. Balychev
Published: 23 April 2022
The publisher has not yet granted permission to display this abstract.
, , Amit Gupta, , Atul Kumar Srivastava, Mitali Srivastava, ,
Published: 21 February 2022
Computational Intelligence and Neuroscience, Volume 2022, pp 1-11; https://doi.org/10.1155/2022/4725639

Abstract:
Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM networks suffer from the parameter tuning problem. Generally, initial and control parameters of LSTM are selected on a trial and error basis. Therefore, in this paper, an evolving LSTM (ELSTM) network is proposed. A multiobjective genetic algorithm (MOGA) is used to optimize the architecture and weights of LSTM. The proposed model is tested on a well-known factory reports dataset. Extensive analyses are performed to evaluate the performance of the proposed ELSTM network. From the comparative analysis, it is found that the LSTM network outperforms the competitive models.
Hui An, Hao Wang, , ,
Published: 7 January 2022
Emerging Markets Review, Volume 51; https://doi.org/10.1016/j.ememar.2021.100878

The publisher has not yet granted permission to display this abstract.
Kexian Zhang, Min Hong
Data Science in Finance and Economics, Volume 2, pp 163-180; https://doi.org/10.3934/dsfe.2022008

Abstract:
As a key input factor in industrial production, the price volatility of crude oil often brings about economic volatility, so forecasting crude oil price has always been a pivotal issue in economics. In our study, we constructed an LSTM (short for Long Short-Term Memory neural network) model to conduct this forecasting based on data from February 1986 to May 2021. An ANN (short for Artificial Neural Network) model and a typical ARIMA (short for Autoregressive Integrated Moving Average) model are taken as the comparable models. The results show that, first, the LSTM model has strong generalization ability, with stable applicability in forecasting crude oil prices with different timescales. Second, as compared to other models, the LSTM model generally has higher forecasting accuracy for crude oil prices with different timescales. Third, an LSTM model-derived shorter forecast price timescale corresponds to a lower forecasting accuracy. Therefore, given a longer forecast crude oil price timescale, other factors may need to be included in the model.
Peng Du, Hong Shu
Published: 1 January 2000
Journal of Global Information Management, Volume 30, pp 1-29; https://doi.org/10.4018/jgim.293286

Abstract:
The purpose is to effectively manage the financial market, comprehensive assess personal credit, reduce the risk of financial enterprises. Given the systemic risk problem caused by the lack of credit scoring in the existing financial market, a credit scoring model is put forward based on the deep learning network. The proposed model uses RNN (Recurrent Neural Network) and BRNN (Bidirectional Recurrent Neural Network) to avoid the limitations of shallow models. Afterward, to optimize path analysis, bionic optimization algorithms are introduced, and an integrated deep learning model is proposed. Finally, a financial credit risk management system using the integrated deep learning model is proposed. The probability of default or overdue customers is predicted through verification on three real credit data sets, thus realizing the credit risk management for credit customers.
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