Research on Credit Evaluation of Financial Enterprises Based on the Genetic Backpropagation Neural Network

Peng, Haitao

doi:10.1155/2021/7745920

Cited by 12 publications

(9 citation statements)

References 14 publications

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“…e basic concept underpinning digital technology's layered modular architecture is that digital products may also become platforms because two firms can compete on the same layer at the same time. Before it can work on a greater level with the digitalization and transformation of education, a new business model is required to examine how the organization generates value for consumers, transmits value, and profits from it [10,14,15]. ey form a potent mechanism that increases firm performance while also providing value to the organization's clientele.…”

Section: Related Workmentioning

confidence: 99%

“…e inherent reliance on subjective judgment or experience judgment, the weight of methods such as analytic hierarchy process (AHP), fuzzy comprehensive evaluation, and differential equation analysis (DEA), among others, results in a lack of long-term perspective and a failure to take into account sustainable development as a result of their design [10,11,[14][15][16][17]. e effects of these changes will be felt for a long time.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation Method for Diversifying Dynamic Capabilities of Small- and Medium-Sized Enterprises Using a Hybrid Genetic Algorithm

Zhao

2022

Mobile Information Systems

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Focusing on the topic of dynamic capability development and digital transformation of traditional enterprises in the digital economy era, and using traditional manufacturing as an example, this paper examines how small- and medium-sized (SMS) businesses should transform and develop in the digital economy era. Furthermore, in light of the problem that existing models cannot evaluate dynamic nonlinear optimization, a diversified dynamic capability evaluation model of SMS enterprises is developed, and the model is solved using the accelerated genetic value method based on real number coding, while the parameters of the BP network are evaluated using the genetic algorithm. In this study, 100 SMS companies are chosen from a pool of 1000 companies to perform an empirical study. In the empirical study, three different scenarios were employed. The selected dataset is utilized to test BP and GA in the first scenario. In the second scenario, the BP and GA are combined to create a more robust model, which is then evaluated on the specified dataset. This research provides a novel approach that combines the PCA with the acceleration method in the third case. According to the findings of this study, the methodology suggested in this work has an overall evaluation accuracy of 95.6 percent, which is pretty good when compared to other approaches. The model training error and test error, on the other hand, are both lower than those of traditional assessment procedures, according to the data.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation Method for Diversifying Dynamic Capabilities of Small- and Medium-Sized Enterprises Using a Hybrid Genetic Algorithm

Zhao

2022

Mobile Information Systems

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“…The traditional method of proportional analysis is still mainly used to evaluate the credit of enterprises in the Republic of China [5]. Traditional enterprise credit evaluation methods like credit rating agencies often consume a large amount of time and manpower but get bad result.…”

Section: Introductionmentioning

confidence: 99%

An Explainable Enterprise Credit Evaluation Method Based on Logistic Regression Integration

Zhai,

Wang,

Xie

et al. 2024

Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27–29,

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Enterprise credit evaluation serves as a necessary process for the construction of social credit economic system. With the increasing requirements for intelligence and efficiency of credit evaluation, machine learning methods are widely employed in credit evaluation. However, the weak interpretability and low data transparency of the current deep neural structure adversely affect the trust between humans and black-box enterprise credit evaluation methods. By revisiting the interpretability in integrated learning, we proposed an interpretable enterprise credit evaluation method with a non-repetitively treesplitting process. Specifically, according to tree splitting method of XGboost, the trees are generated for the learning of linear logistics regression models. Abstracted from the classified trees based on the dataset, the linear logistics regression models are integrated with the interpretable statistic logic, which enables further integration on the interpretable credit evaluation model. Aware of the model consistency, an adaboost (Adaptive Boosting) method is used to integrate linearly weighted linear logistic regression models on different classification trees. Benefits from the consistent evaluation method with strong transparency, our method not only provides interpretable references but also achieves comparable performance. The experiments are conducted on three datasets, including UCI audit dataset, Beijing corporate audit credit and a real in-house dataset. As a result, our model performs comparably with the uninterpretable methods but enables better interprebility.

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“…The AES used BPNN to assist in predicting and classifying. The advantages BPNN has been widely applied, such as in the financial sector [15], civil engineering [16], wireless sensor networks [17], electricity [18]. There are several reasons for choosing AES based on BPNN, such as BPNN having accuracy and precision in making predictions [19], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

An Automated Essay Scoring Based on Neural Networks to Predict and Classify Competence of Examinees in Community Academy

Buditjahjanto¹,

Idhom²,

Munoto³

et al. 2022

TEM Journal

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AES has been widely used in assessing student learning outcomes. However, few studies use Automated Essay Scoring (AES) to simultaneously determine the community academy's competency test scores and levels. This study aims to apply AES to assess essays on the competency certification test. The AES can predict the examinees' scores and classify examinees' competency levels. The method used to build AES uses Back Propagation Neural Networks (BPNN). BPNN was chosen because of its simplicity and ease in building the model. The results showed that the AES for predicting the examinee's competency value showed the MAE value is 0.061621 and the accuracy value is = 97.9665 %. The results of the classification of student competency levels show Accuracy= 0.9063, Precision= 0.9167, Recall= 0.8888, and F1 Score= 0.8857.

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Research on Credit Evaluation of Financial Enterprises Based on the Genetic Backpropagation Neural Network

Cited by 12 publications

References 14 publications

Evaluation Method for Diversifying Dynamic Capabilities of Small- and Medium-Sized Enterprises Using a Hybrid Genetic Algorithm

Evaluation Method for Diversifying Dynamic Capabilities of Small- and Medium-Sized Enterprises Using a Hybrid Genetic Algorithm

An Explainable Enterprise Credit Evaluation Method Based on Logistic Regression Integration

An Automated Essay Scoring Based on Neural Networks to Predict and Classify Competence of Examinees in Community Academy

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