Elements of Credit Rating: A Hybrid Review and Future Research Agenda

Ubarhande, Prashant; Chandani, Arti

doi:10.1080/23311975.2021.1878977

Cited by 19 publications

(6 citation statements)

References 107 publications

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“…The literature shows that under the guarantee fund mechanism, the trading margin paid by market entities with high credit rating may be lower than that of market entities with low credit rating, and may be paid preferentially by the system [14]. In addition, as buyers and sellers with high credit rating are rst associated with the system, the operational risk of market players with good credit in the transaction process is signi cantly reduced.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Research on prediction of power market credit system based on linear model and improved BP neural network

Li¹,

Wang

Yan

2023

Soft Comput

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With the continuous economic growth, the number of power customers has increased signi cantly, and consumers in the eld of power marketing will inevitably have a credit crisis. In order to reduce the business risk of relevant departments and improve the risk prediction ability of the system, this paper evaluates and reviews the user credit system. In this paper, the basic structure of BP neural network is described rstly, and then the traditional BP neural network model is optimized after analyzing its algorithm ow. Based on this point, this paper analyzes the characteristics of customers in the energy and electricity market in the research area, and referring to local experts who have been engaged in power sales for many years, this paper puts forward a new set of directly scored power system load forecasting index system and algorithm improvement scheme, and discusses the evaluation of power market credit rating based on the credit evaluation suggestions of power customers. After establishing the judgment criteria, in this paper, the power load data of the target area is studied by empirical analysis method, and selects three different customers from the production area, commercial and residential areas and residential areas as cases to analyze the determination of their credit rating, and then discusses the results of regional power load forecasting. Finally, this paper puts forward a kind of power management method based on the user's credit rating, and in order to complete the modernization transformation of power management system and promote the market development. In this paper, after improving and optimizing the traditional BP neural network, it is applied to the power market to predict the target user credit system, so as to achieve the improvement of forecasting ability.

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Research on prediction of power market credit system based on linear model and improved BP neural network

Li¹,

Wang

Yan

2023

Soft Comput

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“…With the introduction of various schemes and credit management system, the process of credit management has become more structured and transparent. According to Ubarhande & Chandhani (2021) [10], future credit availability is influenced by risk mitigating ability, risk attached to a project, expected returns from the investment, and payment of loan by the borrowers.…”

Section: Overview Of the Indian Economy And The Debt Taking Comapaniesmentioning

confidence: 99%

Understanding Credit Performance for Financial Health Evaluation

Mishra,

Aithal,

Tripathi

2023

SSRN Journal

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“…The quality of credit rating is of great importance to healthy and ordered development of capital market [6]. Use of advanced techniques such as neural networking and advanced regressions to develop credit-rating model is trend of current times [7]. Crook JN et al [8] analyzed the development of consumer credit evaluation around 2007, mentioning the performance of logistic regression and neural networks in consumer credit assessment.…”

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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Elements of Credit Rating: A Hybrid Review and Future Research Agenda

Cited by 19 publications

References 107 publications

RETRACTED ARTICLE: Research on prediction of power market credit system based on linear model and improved BP neural network

RETRACTED ARTICLE: Research on prediction of power market credit system based on linear model and improved BP neural network

Understanding Credit Performance for Financial Health Evaluation

An Explainable Enterprise Credit Evaluation Method Based on Logistic Regression Integration

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