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

Financial Innovation 2021年第1期7卷 1679-1705页

作者： Pranith Kumar Roy Krishnendu Shaw Department of Management Studies Indian Institute of TechnologyDhanbad 826004JharkhandIndia

Small-and medium-sized enterprises(SMEs)have a crucial influence on the economic development of every nation,but access to formal finance remains a ***,financial institutions encounter challenges in the assessment of SMEs’creditworthiness for the provision of *** institutions employ credit scoring models to identify potential borrowers and to determine loan pricing and collateral *** 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’*** majority of existing models are data-driven and have faced criticism for failing to meet their *** 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 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 *** 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 *** proposed credit scoring model outperformed the existing commercial model in terms of its accuracy in predicting *** model could assist financial institutions,providing a simple means for identifying potential SMEs to grant credit,and advance further research using alternative approaches.

关键词： credit scoring model SME Financial institutions MCDM BWM TOPSIS

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credit scoring by feature-weighted support vector machines

引用

Journal of Zhejiang University-Science C(Computers and Electronics) 2013年第3期14卷 197-204页

作者： Jian SHI Shu-you ZHANG Le-miao QIU The State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University School of Electrical and Automatic Engineering Changshu Institute of Technology

Recent finance and debt crises have made credit risk management one of the most important issues in financial *** credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and *** this paper,a novel feature-weighted support vector machine(SVM) credit scoring model is presented for credit risk assessment,in which an F-score is adopted for feature importance *** the mutual interaction among modeling features,random forest is further introduced for relative feature importance *** two feature-weighted versions of SVM are tested against the traditional SVM on two real-world datasets and the research results reveal the validity of the proposed method.

关键词： credit scoring model Support vector machine(SVM) Feature weight Random forest

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