A multicriteria decision support tool for modelling bank credit ratings

Gaganis, Chrysovalantis; Papadimitri, Panagiota; Tasiou, Menelaos

doi:10.1007/s10479-020-03516-9

Cited by 22 publications

(7 citation statements)

References 52 publications

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“…Fuzzy TOPSIS technique based on fuzzy sets in determining the multiple criteria selection issues for the credit scoring process. Froelich and Hajek (2020) Improvement of multi-criteria group decision-making technique for bank credit risk assessment combined with traditional TOPSIS approach to ranking alternatives Gaganis et al (2020) A Stochastic Multiobjective Acceptability Analysis -Fuzzy-FlowSort model, the first variant of PROMETHEE-based sorting methods, is employed in order to manage the credit risks of 55 European banks. Hassanzadeh and Valmohammadi (2021) The assessment and rank of the Tehran stock market's banks is performed by combining the fuzzy AHP, for calculating the weights of each criteria considered, with the TOPSIS techniques to rank the banks.…”

Section: Mehdiyev (2019)mentioning

confidence: 99%

Credit Risk Management of Property Investments through Multi-Criteria Indicators

Locurcio

Tajani

Morano

et al. 2021

Risks

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The economic crisis of 2008 has highlighted the ineffectiveness of the banks in their disbursement of mortgages which caused the spread of Non-Performing Loans (NPLs) with underlying real estate. With the methods stated by the Basel III agreements, aimed at improving the capital requirements of banks and determining an adequate regulatory capital, the banks without the skills required have difficulties in applying the rigid weighting coefficients structures. The aim of the work is to identify a synthetic risk index through the participatory process, in order to support the restructuring debt operations to benefit smaller banks and small and medium-sized enterprises (SME), by analyzing the real estate credit risk. The proposed synthetic risk index aims at overcoming the complexity of Basel III methodologies through the implementation of three different multi-criteria techniques. In particular, the integration of objective financial variables with subjective expert judgments into a participatory process is not that common in the reference literature and brings its benefits for reaching more approved and shared results in the debt restructuring operations procedure. Moreover, the main findings derived by the application to a real case study have demonstrated how important it is for the credit manager to have an adequate synthetic index that could lead to the avoidance of risky scenarios where several modalities to repair the credit debt occur.

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Section: Mehdiyev (2019)mentioning

confidence: 99%

Credit Risk Management of Property Investments through Multi-Criteria Indicators

Locurcio

Tajani

Morano

et al. 2021

Risks

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“…In academic articles, the banks' performances were evaluated using various multicriteria methods for the financial field [29][30][31][32][33], for nonfinancial factors [34,35], and by those studies which considered both categories [36,37]. A few methods are appropriate for the analysis of these studies.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of Green Banking Performance

Lapinskienė,

Danilevičienė

2023

Sustainability

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Internal and external pressures are pushing the financial system towards an increasingly environmentally responsible orientation. The damaging practices of green-washing necessitate the search for new ways of monitoring. The question then arises, how can one measure the actual degree of greenness of a banking industry? This study aims to create a new methodology framework to measure green performance in the banking industry using multi-criteria methods. We offer a theoretical contribution. First, a set of criteria was identified at the theoretical level. Second, the criteria were evaluated by practitioners and aggregated using the ‘TOPSIS’ method. This index may constitute a basis for ranking banks. The results showed that the most important factors to be considered when evaluating the performance of green banking are the greenness of the customers, the development of innovations leading to a green economy, the availability of green financial products and services, and the promotion of green education. These results lead to the conclusion that both banks and clients should become “greener” and utilize more green innovations and financial products/services.

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“…Similarly, the nonfinancial criteria were grouped into three main categories of industry and business evaluation, management evaluation, and conduct of account evaluation and were further subdivided into relevant nonfinancial information. In addition, this study adopted an approach to the relevant nonfinancial information based on studies like Kim and Sohn (2004), Angilella and Mazzù (2015), Ignatius et al (2018), Gaganis et al (2020), andFroelich andHajek (2020).…”

Section: Phase-i: Selection Of Criteria For Rating Smesmentioning

confidence: 99%

A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS

Roy

Shaw

2021

Financ Innov

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Small- and medium-sized enterprises (SMEs) have a crucial influence on the economic development of every nation, but access to formal finance remains a barrier. Similarly, financial institutions encounter challenges in the assessment of SMEs’ creditworthiness for the provision of financing. Financial institutions employ credit scoring models to identify potential borrowers and to determine loan pricing and collateral requirements. SMEs are perceived as unorganized in terms of financial data management compared to large corporations, making the assessment of credit risk based on inadequate financial data a cause for financial institutions’ concern. The majority of existing models are data-driven and have faced criticism for failing to meet their assumptions. To address the issue of limited financial record keeping, this study developed and validated a system to predict SMEs’ credit risk by introducing a multicriteria credit scoring model. The model was constructed using a hybrid best–worst method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Initially, the BWM determines the weight criteria, and TOPSIS is applied to score SMEs. A real-life case study was examined to demonstrate the effectiveness of the proposed model, and a sensitivity analysis varying the weight of the criteria was performed to assess robustness against unpredictable financial situations. The findings indicated that SMEs’ credit history, cash liquidity, and repayment period are the most crucial factors in lending, followed by return on capital, financial flexibility, and integrity. The proposed credit scoring model outperformed the existing commercial model in terms of its accuracy in predicting defaults. This model could assist financial institutions, providing a simple means for identifying potential SMEs to grant credit, and advance further research using alternative approaches.

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A multicriteria decision support tool for modelling bank credit ratings

Cited by 22 publications

References 52 publications

Credit Risk Management of Property Investments through Multi-Criteria Indicators

Credit Risk Management of Property Investments through Multi-Criteria Indicators

Assessment of Green Banking Performance

A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS

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