Developing an integrated fuzzy credit rating system for SMEs using fuzzy-BWM and fuzzy-TOPSIS-Sort-C

Roy, Pranith Kumar; Shaw, Krishnendu; Ishizaka, Alessio

doi:10.1007/s10479-022-04704-5

Cited by 16 publications

(7 citation statements)

References 62 publications

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“…The strength of MCDM lies in its ability to rank decisions (Rai et al ., 2022). The representative MCDM are analytic hierarchy process (AHP) (Duong et al ., 2020), analytic network process (ANP) (Fosso Wamba et al ., 2023), Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) (Ghadge et al ., 2021), technique for order preference by similarity to an ideal solution (TOPSIS) (Kumar Roy et al ., 2022), elimination and choice translating reality (ELECTRE) (Doumpos and Figueira, 2019), decision making trial and evaluation laboratory (DEMATEL) (He et al ., 2021), best worst method (BWM) (Kumar Roy et al ., 2022), fuzzy set theory (FS) (Guo et al ., 2020) and grey theory (Mao et al ., 2020), etc. The majority of individual MCDM methods ignored the effect of uncertainty inherent to decision problems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…MCDM provides a solid methodological basis to resolve the inherent multi-criteria nature of the problem (Cui et al, 2022). The strength of MCDM lies in its ability to rank decisions (Rai et al, 2022).The representative MCDM are analytic hierarchy process (AHP) (Duong et al, 2020), analytic network process (ANP) (Fosso Wamba et al, 2023), Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) (Ghadge et al, 2021), technique for order preference by similarity to an ideal solution (TOPSIS) (Kumar Roy et al, 2022), elimination and choice translating reality (ELECTRE) (Doumpos and Figueira, 2019),…”

Section: Literature Review 21 Credit Risk Evaluationmentioning

confidence: 99%

“…Bayesian network decision making trial and evaluation laboratory (DEMATEL) (He et al, 2021), best worst method (BWM) (Kumar Roy et al, 2022), fuzzy set theory (FS) (Guo et al, 2020) and grey theory (Mao et al, 2020), etc. The majority of individual MCDM methods ignored the effect of uncertainty inherent to decision problems.…”

Section: The Dynamicmentioning

confidence: 99%

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A decision support method for credit risk based on the dynamic Bayesian network

Lu,

Wu,

Dong

et al. 2023

IMDS

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PurposeCredit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.Design/methodology/approachFirst, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.FindingsCompared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.Originality/valueThe authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Review 21 Credit Risk Evaluationmentioning

confidence: 99%

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A decision support method for credit risk based on the dynamic Bayesian network

Lu,

Wu,

Dong

et al. 2023

IMDS

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show abstract

“…Roy and Shaw (2021) developed a credit score model for small and medium‐sized enterprises, which uses hybrid analytic hierarchy process‐technique (AHP) to calculate criteria and subcriteria weights, and then uses TOPSIS to determine credit scores. Kumar Roy et al (2022) used FUZZY‐BWM to determine the index weight, combined with TOPSIS, and a multi‐criteria credit rating system for small and medium‐sized enterprises is proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

EWT‐SMOTE to improve default prediction performance in imbalanced data: Analysis of Chinese data

Zhou,

Lin,

Chi

et al. 2023

Journal of Forecasting

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This study aims to solve the imbalanced sample problem in default prediction. We calculate the classification contribution score of each default customer by the entropy weight technique (EWT) for order of preference by similarity to the ideal solution and construct a default prediction model according to several models. Our proposed EWT‐synthetic minority oversampling technique (SMOTE) method significantly improves the prediction accuracy of several typical default prediction models and reduces type II error. We find that the indicators “net cash flow from operating activities,” “Engel coefficient,” “basic earnings per share,” and “total social retail sales” significantly influence default prediction of Chinese listed companies.

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“…This amalgamation not only yields a comprehensive and accurate evaluation of the importance of each indicator but also ensures that various types of indicators are treated equitably in the decision-making process. Through comprehensive ranking, it provides decision-makers with scientifically informed support for quantitative analysis-based decision-making, aiding effective evaluation and selection of optimal risk management strategies [48]. As the financial risk decreases, a company tends to approach the optimal solution and distance itself from the worst solution.…”

Section: A Risk Measurement Of Critic-topsismentioning

confidence: 99%

A Hybrid Approach for Predicting Corporate Financial Risk: Integrating SMOTE-ENN and NGBoost

Zhu,

Hu,

Liu

et al. 2023

IEEE Access

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The financial condition of an enterprise is a critical factor in determining its viability and longterm prospects, and it also directly affects the interests of stakeholders such as investors and creditors. So extra attention is needed to evaluate and forecast the financial risk of an enterprise. This paper proposes a novel enterprise financial risk evaluation and prediction model integrating Synthetic Minority Over-sampling and Edited Nearest Neighbors (SMOTE-ENN) and Natural Gradient Boosting (NGBoost). The CRITIC-TOPSIS method is first employed to conduct a comprehensive evaluation of the financial risk of the listed companies based on their annual financial statements. Subsequently, the obtained evaluation results are subjected to a clustering analysis to categorize the companies into different levels of financial risk. To address the issue of imbalanced samples, the study introduces the SMOTE-ENN combined sampling method. This technique is applied to mitigate the imbalance in the number of samples across different categories. Lastly, NGBoost is utilized to develop a prediction model for corporate financial risk based on the clustered data. The analysis reveals that companies with low levels of financial risk typically exhibit a balanced financial profile, robust operational performance, and substantial growth potential. Conversely, companies with high levels of financial risk are characterized by weak profitability and debt-paying ability. The experimental findings show that the model has a 97.18% accuracy rate, thereby suggesting that the model is both reasonable and practicable.

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Developing an integrated fuzzy credit rating system for SMEs using fuzzy-BWM and fuzzy-TOPSIS-Sort-C

Cited by 16 publications

References 62 publications

A decision support method for credit risk based on the dynamic Bayesian network

A decision support method for credit risk based on the dynamic Bayesian network

EWT‐SMOTE to improve default prediction performance in imbalanced data: Analysis of Chinese data

A Hybrid Approach for Predicting Corporate Financial Risk: Integrating SMOTE-ENN and NGBoost

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