Credit Risk Of Small And Medium-sized Enterprises Research Articles

Forecasting Credit Risk of SMEs in Supply Chain Finance Using Bayesian Optimization and XGBoost

Supply chain finance plays a crucial role as a financing channel for small‐ and medium‐sized enterprises (SMEs). However, issues such as financial problems and credit defaults have led to disruptions in this channel. To address credit risk control in SME financing within the field of supply chain finance, this paper focuses on a sample of 506 equipment manufacturing companies listed on the SME board of the Shenzhen Stock Exchange from 2016 to 2020. Taking into consideration, the overall risks faced by these enterprises, the study establishes seven first‐level indicators and identifies 84 candidate second‐level indicators. Partial correlation and variance analysis are then used for the first round of indicator screening, followed by the use of a BP neural network for the second round of selection. As a result, a system of 26 indicators for supply chain financial risk is constructed. The XGBoost model is employed to evaluate the constructed risk index system, while SVM and random forest models are used as comparison models. Bayesian optimization is utilized for parameter tuning of the three models. Empirical results demonstrate that the BO‐XGBoost model reduces prediction errors in comparison to the control models. Furthermore, statistical tests reveal that the predicted values of the BO‐XGBoost model significantly differ from those of the other control models. Compared to other individual models, the BO‐XGBoost model exhibits increased accuracy in credit risk prediction and a significant discriminative effect. These findings highlight the effectiveness of constructing an efficient risk indicator system and utilizing Bayesian optimization for parameter tuning in XGBoost to better differentiate between risky and normal enterprises, thereby minimizing default losses. The research results underscore the advantages of employing Bayesian optimization in XGBoost, which can be applied in credit default prediction for SMEs and serves as a valuable tool in financial risk management and control.

Modeling SMEs Credit Default Risk: The Case of Saudi Arabia

This study assesses the credit risk of small and medium-sized enterprises (SMEs) to minimize unexpected risk events. We construct a hybrid statistical model based on factor analysis and logistic regression to predict enterprise default on loans and determine the factors predicting SMEs default. We assess the credit risk of SMEs listed on the Saudi stock market. The results indicate that the SMEs acid-test ratios are the most influential factors in predicting SMEs credit risk. Therefore, the designed logistic model can be used by financial institutions during the decision-making process of granting loans to SMEs. This study sheds light on challenging access to bank credits due to the lack of financial transparency of most Saudi SMEs.

Clues from networks: quantifying relational risk for credit risk evaluation of SMEs

Owing to information asymmetry, evaluating the credit risk of small- and medium-sized enterprises (SMEs) is difficult. While previous studies evaluating the credit risk of SMEs have mostly focused on intrinsic risk generated by SMEs, our study considers both intrinsic and relational risks generated by neighbor firms’ publicly available risk events. We propose a framework for quantifying relational risk based on publicly available risk events for SMEs’ credit risk evaluation. Our proposed framework quantifies relational risk by weighting the impact of publicly available risk events of each firm in an interfirm network—considering the impact of interfirm network type, risk event type, and time dependence of risk events—and combines the relational risk score with financial and demographic features to evaluate SMEs credit risk. Our results reveal that relational risk score significantly improves both discrimination and granting performances of credit risk evaluation of SMEs, providing valuable managerial and practical implications for financial institutions.

Credit Risk Evaluation in Enterprise Financial Management by Using Convolutional Neural Network under the Construction of Smart City

Smart cities are the forward goals of future cities, and the development of small- and medium-sized enterprises (SMEs) plays a key role in it. To solve the financing difficulties of SMEs, it is necessary to scientifically, objectively, and accurately assess the credit risk of SMEs. Based on the current research and analysis of scholars in related fields, taking corporate financial data as the object, the SMEs’ credit risk is assessed through an adaptive and self-learning convolutional neural network (CNN) method. The GoogleNet method is further improved to reconstruct the credit risk evaluation model of SMEs. Finally, the influence of the convolution kernel depth on the accuracy is discussed through the optimization of the structure and the selection of the initial value of the learning rate. The results are verified by using the data in the 2016–2021 SME sector in Shanghai and Shenzhen in the wind database. It can be found that when the learning rate is finally set to 0.4 and the convolution kernel depth is set to 8 and 32 in turn, the evaluation accuracy of the test dataset is the highest, with a total accuracy of 96.8%. Therefore, it is believed that the newly constructed model has higher accuracy and more practical value than the other three typical risk assessment models.

Credit Risk Early Warning of Small and Medium-Sized Enterprises Based on Blockchain Trusted Data

Small and medium-sized enterprises (SMEs) are now growing rapidly and playing an important role in the development of the national economy. As the economy grows, the contradiction between the credit risk of SMEs and the credit risk early warning mechanism of traditional supply chain financing has become increasingly important. In response to the issues of a single source of business information, the high investment cost of the existing early risk early warning mechanism, etc., from a commercial bank credit risk management perspective, this paper proposes to build an SMEs credit risk early warning system based on reliable blockchain data. The reliability of the data obtained is assessed utilising a hierarchical analysis and a vague overall judgement method. The results show that the use of blockchain technology can enhance the credibility and accuracy of the data, which provides a data guarantee for more rapid risk alert.

Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data

The credit risk of small and medium-sized enterprises (SMEs) in supply chain finance (SCF) is defined as the probability that the SME would default on loans derived from financing for the SCF platform. Traditional models make use of merely the static data of SMEs, such as enterprise demographic data and financial statement data, to predict the credit risk of SMEs in SCF. Nevertheless, behavioral data, which reflect the dynamic financing behavior of SMEs in SCF, are overlooked by these models, which limits the performance of credit risk prediction. To address this problem, a novel approach is proposed called DeepRisk to fuse enterprise demographic data and financing behavioral data to predict the credit risk of SMEs in SCF. We adopt the multi-modal learning strategy to fuse the two different sources of data. The concatenated vectors derived from data fusion are then used as the input of the feed forward neural network to predict the credit risk of SMEs. Experiments on a real SCF dataset demonstrate that the proposed DeepRisk approach outperforms the baseline methods in credit risk prediction in terms of precision, recall, F1-score, area under curve (AUC), and economic loss. The fusion of the two different sources of data is superior to the existing approaches to the credit risk prediction of SMEs in SCF. Both the static enterprise demographic data and the dynamic financing behavioral data are crucial to improve the credit risk prediction of SMEs. Nevertheless, the variables derived from the financing behavioral data have a better predictability than those from the enterprise demographic data. Managerial implications have been identified for decision makers involved in SCF in utilizing the benefits of SCF and in managing their credit risks.

Non-financial variables related to governance and financial distress prediction in SMEs–evidence from Egypt

PurposeThis study examines the effect of non-financial variables related to governance on the accuracy of financial distress prediction among Egyptian listed small and medium-sized enterprises (SMEs), by using the logistic regression technique.Design/methodology/approachThis study used a sample of 24 Egyptian-listed SMEs in each year, totaling 120 firm observations, of which 25 were classified distressed and 95 of them non-distressed between 2014 and 2018. The variables for the study included five financial variables and thirteen non-financial variables related to governance. The models were developed using financial variables alone as well as combining financial and non-financial variables related to governance.FindingsThe results showed that the model with financial variables had a prediction accuracy of 91.7% , whereas models with a combination of financial and non-financial variables related to governance predict with comparatively better accuracy of 92.7 and 93.6% .Research limitations/implicationsAlthough the results seem to be conclusive, it could be noted that the non-distressed sample was not paired with the distressed sample. Other studies showed that paired samples increase the financial distress prediction rate. Furthermore, due to the small sample size, this study was unable to create a hold-out sub-sample for the accuracy test.Practical implicationsThe proposed distress prediction model for SMEs is effective for stakeholders, including banks and other financial institutions, in the assessment of the credit risk of SMEs. Using such a model, they could better identify SMEs with a higher risk of failure in their lending decisions. Moreover, SME managers' could be interested in using such models as a tool for planning corrective action, in addition to planning and controlling current operations to avoid financial failure in the future.Originality/valueThis study contributes to financial distress prediction literature in different ways. First, few studies were conducted in the area of financial distress among SMEs. Second, neither of these studies was conducted within the Egyptian context, nor any of them had used non-financial variables related to governance in the prediction of financial distress among SMEs.

Identifying and Predicting the Credit Risk of Small and Medium-Sized Enterprises in Sustainable Supply Chain Finance: Evidence from China

COVID-19 has created a strong demand for supply chain finance (SCF) for small and medium-sized enterprises (SMEs). However, the rapid development of SCF leads to more complex credit risks. How to effectively discriminate and manage SMEs to reduce credit risk has become one of the most critical issues in SCF. In addition, sustainable SCF (SSCF) has received increasing attention, and credit risk management is important to achieve SSCF. Therefore, it is significant to identify the key factors influencing the credit risk of SMEs and construct a prediction model to promote SSCF. This study uses the lasso-logistic model to identify factors influencing the credit risk of SMEs and to predict the credit risk of SMEs. The empirical results show that (i) the key factors influencing SMEs’ credit risk include six variables—the matching degree of order data, ratio of contract enforcement, number of contract defaults, degree of business concentration, and number of administrative penalties; and (ii) the lasso-logistic model can identify the key factors influencing credit risk and have a better prediction performance. Moreover, transaction credit and reputation supervision significantly influence the credit risk of SMEs.

Multiview Graph Learning for Small‐ and Medium‐Sized Enterprises’ Credit Risk Assessment in Supply Chain Finance

In recent years, supply chain finance (SCF) is exploited to solve the financing difficulties of small‐ and medium‐sized enterprises (SMEs). SME credit risk assessment is a critical part in the SCF system. The diffusion of SME credit risk may cause serious consequences, leading the whole supply chain finance system unstable and insecure. Compared with traditional credit risk assessment models, the supply chain relationship, credit condition of SME, and core enterprises should all be considered to rate SME credit risk in SCF. Traditional methods mix all indicators from different index systems. They cannot give a quantitative result on how these index systems work. Furthermore, traditional credit risk assessment models are heavily dependent on the number of annotated SME data. However, it is implausible to accumulate enough credit risky SMEs in advance. In this paper, we propose an adaptive heterogenous multiview graph learning method to tackle the small sample size problem for SMEs’ credit risk forecasting. Three graphs are constructed by using indicators from supply chain operation, SME financial indicator, and nonfinancial indicator individually. All the graphs are integrated in an adaptive manner, providing a quantitative explanation on how the three parts cooperate. The experimental analysis shows that the proposed method has good performance for determining whether SME is risky or nonrisky in SCF. From the perspective of SCF, SME financing ability is still the main factor to determine the credit risk of SME.

Evaluating the credit risk of SMEs using legal judgments

Loan application assessments of small and medium-sized enterprises (SMEs) are difficult because of information asymmetry. To mitigate the information asymmetry, this paper focuses on information found in legal judgments involving the company and its principles and combines this information with financial and firm-specific information to help evaluate the credit risk of SMEs. We propose a framework to identify legal judgments that are effective in predicting credit risk and extract relevant features that are contained within the effective legal judgments. Empirical evaluation shows that features extracted from effective legal judgments significantly improve the discrimination performance and granting performance of our model compared with the baseline model, which uses financial and firm-specific features only.

Capital structure and firm performance in European SMEs

PurposeThe purpose of this paper is to investigate whether the relationship between capital structure and firm performance in small- and medium-sized enterprises (SMEs) is moderated by credit risk.Design/methodology/approachThe authors empirically test whether an SME’s credit risk affects the SME’s relationship between capital structure and firm performance by using a 2012 cross-sectional sample of European SMEs from Austria, Belgium, Finland, France, Germany, Italy, Portugal, Spain, Sweden and the UK.FindingsThe empirical results suggest that in low credit risk SMEs, the debt ratio is negatively related to firm performance; however, this relationship is not present in high credit risk SMEs. Therefore, it is indicated that SME credit risk moderates the relationship between capital structure and firm performance.Practical implicationsThe findings of the paper will enable financial managers to understand the importance of SMEs’ credit risk and will assist them in maximizing firms’ performance.Originality/valueThis paper extends the findings of previous studies by examining whether credit risk affects the relationship between capital structure and firm performance.

Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models

Based on logistic regression (LR) and artificial neural network (ANN) methods, we construct an LR model, an ANN model and three types of a two-stage hybrid model. The two-stage hybrid model is integrated by the LR and ANN approaches. We predict the credit risk of China’s small and medium-sized enterprises (SMEs) for financial institutions (FIs) in the supply chain financing (SCF) by applying the above models. In the empirical analysis, the quarterly financial and non-financial data of 77 listed SMEs and 11 listed core enterprises (CEs) in the period of 2012–2013 are chosen as the samples. The empirical results show that: (i) the “negative signal” prediction accuracy ratio of the ANN model is better than that of LR model; (ii) the two-stage hybrid model type I has a better performance of predicting “positive signals” than that of the ANN model; (iii) the two-stage hybrid model type II has a stronger ability both in aspects of predicting “positive signals” and “negative signals” than that of the two-stage hybrid model type I; and (iv) “negative signal” predictive power of the two-stage hybrid model type III is stronger than that of the two-stage hybrid model type II. In summary, the two-stage hybrid model III has the best classification capability to forecast SMEs credit risk in SCF, which can be a useful prediction tool for China’s FIs.

Comparison of individual, ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance

Supply chain finance (SCF) becomes more important for small- and medium-sized enterprises (SMEs) due to global credit crunch, supply chain financing woes and tightening credit criteria for corporate lending. Currently, predicting SME credit risk is significant for guaranteeing SCF in smooth operation. In this paper, we apply six methods, i.e., one individual machine learning (IML, i.e., decision tree) method, three ensemble machine learning methods [EML, i.e., bagging, boosting, and random subspace (RS)], and two integrated ensemble machine learning methods (IEML, i.e., RS–boosting and multi-boosting), to predict SMEs credit risk in SCF and compare the effectiveness and feasibility of six methods. In the experiment, we choose the quarterly financial and non-financial data of 48 listed SMEs from Small and Medium Enterprise Board of Shenzhen Stock Exchange, six listed core enterprises (CEs) from Shanghai Stock Exchange and three listed CEs from Shenzhen Stock Exchange during the period of 2012–2013 as the empirical samples. Experimental results reveal that the IEML methods acquire better performance than IML and EML method. In particular, RS–boosting is the best method to predict SMEs credit risk among six methods.

AN IDIOSYNCRATIC DECISION SUPPORT SYSTEM FOR CREDIT RISK ANALYSIS OF SMALL AND MEDIUM-SIZED ENTERPRISES

Small and medium-sized enterprises (SMEs) are currently considered an important driving force of economic growth. Several studies have been developed to analyse this issue and, in particular, to assess the credit risk of SMEs. Most of these applications, however, share the same methodological limitations, such as the manner by which criteria are selected, or the methods used for calculating the weights between them. Based on the integrated use of cognitive mapping techniques and the Interactive Multiple Criteria Decision Making (TODIM) approach, this study aims to create an idiosyncratic decision support system for the identification of multiple criteria and the calculation of their respective weights (i.e. the trade-offs) in the evaluation of SME credit risk. The results show that the model created in this study allows for simple and straightforward credit concession decisions, facilitating the evaluation of SME credit applications through more informed and transparent risk assessments. Practical implications, strengths and weaknesses of the proposed framework are analysed and discussed.