Credit Risk Evaluation Research Articles

A Meta-Path-Based Evaluation Method for Enterprise Credit Risk.

Nowadays, small and medium-sized enterprises (SMEs) have become an essential part of the national economy. With the increasing number of such enterprises, how to evaluate their credit risk becomes a hot issue. Unlike big enterprises with massive data to analyse, it is hard to find enough primary information of SMEs to assess their financial status, which makes the credit risk evaluation result less accurate. Limited by the lack of primary data, how to infer SMEs' credit risk from secondary data, such as information about their upstream, downstream, parent, and subsidiary enterprises, attracts big attention from industry and academy. Targeting on accurately evaluating the credit risk of the SME, in this study, we exploit the representative power of the information network on various kinds of SME entities and SME relationships to solve the problem. With that, a heterogeneous information network of SMEs is built to mine enterprise's secondary information. Furthermore, a novel feature named meta-path feature is proposed to measure the credit risk, which makes us able to evaluate the financial status of SMEs from various perspectives. Experiments show that our proposed meta-path feature is effective to identify SMEs with credit risks.

Credit risk evaluation: a comprehensive study

Credit risk evaluation: a comprehensive study

How Banks Can Leverage Credit Risk Evaluation to Improve Financial Performance

The research aims to examine the impact of credit risk evaluation on the financial performance of American and European commercial banks during the period 2017-2021. A set of 37 commercial banks were selected to represent the entire banking industry of those two continents. To measure this relationship, two mathematical models were created. Research has revealed that credit risk evaluation influences the financial performance of the American and European commercial banks as represented by ROE and ROA. The study also concludes that the credit risk evaluation indicators analyzed in this study have a substantial effect on the financial performance of American and European commercial banks. The study suggests banks enhance their credit risk evaluation to generate more profits. It also cites the indicators of non-performing loans or gross loans, provision for facilities loss/net facilities, as well as the leverage ratio as significant in determining credit risk evaluation. Banks must put together strategies that will not only limit the banks’ exposure to credit risk, but also enhance the banks’ performance, as well as competitiveness. Further research can be conducted in developing nations to understand the impact of credit risk evaluation in such economies.

Early Warning Signals on Credit Risk Mitigation among SME’s in Malaysia

By combining financial and non-financial characteristics, the research creates a credit risk mitigation model for SME's. These variables were used to analyse the impact of owner education level, gender, and business age as an early warning signal to the credit risk mitigation. To analyse the impact of owner education level, gender, and business age as an early warning signal to the credit risk mitigation, the research developed a credit risk mitigation model for SME's by combining financial and non-financial characteristics. Utilizing all relevant information, a Multiple Discriminant Analysis (MDA) model was applied. 400 observations make up the final sample for the estimation model, of which half are distressed and non-distressed SME’s, for the years 2015 to 2020. The prediction models perform relatively well in the financial and non-financial variables. This evidence shows that the models serve as efficient early warning signals and can thus be beneficial for monitoring and evaluation of credit risk.

Credit Risk Evaluation of Supply Chain Finance Based on K-Means-SVM Model

With the rapid development of supply chain finance, it is important to evaluate its credit risk effectively. The Support Vector Machine (SVM) is designed to construct the credit risk measurement model of supply chain finance. Considering the characteristics of SVM model, we select the clustering center based on K-Means clustering algorithm and the edge points far from the clustering center as training samples to train the SVM model. Experimental results show that compared with single SVM model, the overall classification accuracy of K-means-SVM model is increased by 7.2%, and the first type error rate is reduced by 5.0%, which verifies the superiority and effectiveness of k-means-SVM model applied to enterprise credit risk assessment under supply chain finance mode.

Credit Risk Evaluation of Supply Chain Finance Based on K-Means-SVM Model

With the rapid development of supply chain finance, it is important to evaluate its credit risk effectively. The Support Vector Machine (SVM) is designed to construct the credit risk measurement model of supply chain finance. Considering the characteristics of SVM model, we select the clustering center based on K-Means clustering algorithm and the edge points far from the clustering center as training samples to train the SVM model. Experimental results show that compared with single SVM model, the overall classification accuracy of K-means-SVM model is increased by 7.2%, and the first type error rate is reduced by 5.0%, which verifies the superiority and effectiveness of k-means-SVM model applied to enterprise credit risk assessment under supply chain finance mode.

Credit Risk Prediction for Peer-To-Peer Lending Platforms: An Explainable Machine Learning Approach

Small and medium enterprises face the challenge of obtaining start-up fund due to the strict rules and conditions set by banks and financial institutions. The plight yields to the growth in popularity of online peer-to-peer lending platforms which are an easier way to obtain loan as they have fewer rigid rules. However, high flexibility of loan funding in peer-to-peer lending comes with high default probability of loan funded to high-risk start-ups. An efficient model for evaluating credit risk of borrowers in peer-to-peer lending platforms is important to encourage investors to fund loans and justify the rejection of unsuccessful applications to satisfy financial regulators and increase transparency. This paper presents a supervised machine learning model with logistic regression to address this issue and predicts the probability of default of a loan funded to borrowers through peer-to-peer lending platforms. In addition, factors that affect the credit levels of borrowers are identified and discussed. The research shows that the most important features that affect probability of default are debt-to-income ratio, number of mortgage account, and Fair, Isaac and Company Score.

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 Evaluation of Asset Securitization of PPP Project of Sports Public Service Venues Based on Random Forest Algorithm.

Due to the characteristics of sports public service venues, there are still some financing difficulties under the PPP (public-private partnership) operation mode. Although asset securitization can solve its corresponding problems and enhance the standardization of PPP projects, its many participants, complex transaction structure, and other influencing factors still have a great impact on the financing effect. Therefore, this paper integrates the random forest algorithm into the asset securitization credit risk evaluation system of PPP project of sports public service venues, and screens the audition indicators of credit risk evaluation through the constructed model. The experimental results show that optimizing the parameter setting of random forest model can effectively reduce the misjudgment rate of default samples, while maintaining the stability of other performance indicators. Even if other misjudgment rates will increase to a certain extent, it is within a reasonable range, which improves the overall performance of the model. The ROC curve shows that the risk credit indicators selected by the model have strong evaluation performance and effectiveness and can provide some reference information for the credit risk prevention of asset securitization of PPP project of sports public service venues.

Online Loan Default Prediction Model Based on Deep Learning Neural Network.

With the rapid development of Internet loans and the demand for Internet loans, Internet-based loan default prediction is particularly important. P2P online lending is based on Internet technology. With the popularization of personal PCs and mobile terminals, the borrower's financing cost has been reduced to a large extent, and the efficiency of the borrower's capital utilization has also been improved to a considerable level. Making full use of the existing data of the online lending platform, integrating third-party data, and predicting the default behavior of users are the major directions of future development. This paper mainly studies the network loan default prediction model based on DPNN. This paper first analyzes the problems and risks of the P2P online lending platform, then introduces the principle and characteristics of BPNN in detail, and determines the credit risk rating process for online lending based on BPNN. With the help of data analysis and processing software, after cleaning and variable selection of credit customer data provided by lending clubs, a set of corresponding online lending default risk assessment models are established through BPNN. This paper simulates the network loan default assessment model of the BPNN model and compares it with the support vector machine and regression model. The experimental results show that the highest accuracy rate of the BPNN model is 98.01% and the highest recall rate is 99.82%, which is better than the other two models; the AUC value of BPNN is 0.79, which is significantly higher than that of support vector machine and regression model. The above results show that the online loan default prediction model based on DPNN has high application value in practice. Predicting the probability of customer default risk in advance will help reduce the risk of P2P companies and lenders, improve the competitiveness of P2P lending institutions, and promote the development of domestic P2P platforms to be more stable.

Risk Assessment of Operator’s Big Data Internet of Things Credit Financial Management Based on Machine Learning

Credit risk evaluation innovation is of incredible importance to monetary establishments. AI innovation can fundamentally work on the precision and versatility of credit risk evaluation. This paper aims to study the risk assessment of operator big data Internet of Things credit financial management based on machine learning. It proposes machine learning-related algorithms, including the introduction of logistic model and decision tree model, as well as related concepts of credit financial management risk. This paper proposes that big data can be better used to reduce financial risk management problems and proposes specific actions based on the actual situation of the company. This paper selects company A for financial risk management evaluation through case analysis and compares it with three major e-commerce companies. The experimental results show that the earnings per share of company A is between −0.99 and 0. Company A is still in a state of loss in recent years, and there are certain debt risks, operational risks, and capital risks.

Construction of Artificial Intelligence Application Model for Supply Chain Financial Risk Assessment

An artificial intelligence integrated application model of supply chain financial risk assessment is constructed. Based on the financial data and supply chain data of listed companies in China’s new energy electric vehicle industry, the supply chain financial credit risk evaluation index system is constructed. The data samples are preprocessed by PCA as the input data of the support vector machine, which effectively solves the problem of high-dimensional data in supply chain finance. By improving the inertia weight of particle swarm optimization and introducing mutation operation, a dynamic mutation particle swarm optimization algorithm is proposed to avoid the problem of particles falling into a local minimum in the process of optimization. Finally, the improved optimization algorithm is used to optimize the parameters of SVM and input AdaBoost integration as a weak classifier to build an integrated model with good performance in many aspects. The model has been successfully applied to the credit risk assessment of China’s new energy vehicle supply chain finance. The comparison with other models shows that the constructed model has certain advantages in performance.

Credit default risk in Islamic and conventional banks: Evidence from a GARCH option pricing model

An important question in banking is whether restrictions placed on Islamic banks make them more resilient to financial market turmoil and less prone to failure than conventional banks. We evaluate this claim by estimating credit default risk measures for a sample of conventional and Islamic banks using a GARCH option pricing model. Using a daily data set that is better suited for the time variation in volatility, we calculate distance to default measures to evaluate credit risk of Conventional Banks (CBs) and Islamic banks (IBs). We find higher default risk measures for IBs than CBs in general except during the Global Financial Crisis. This result holds true after controlling for bank and country specific variables in that IBs seem to have significantly lower default risk during the Global Financial Crisis and higher default risk thereafter. Consequently, while restrictions on risk taking is advantageous in financial turmoil episodes, the same restrictions expose IBs to risks in normal times. Finally, the credit risk of CBs and IBs is negatively affected by the oil crisis in 2014–2015 and the Covid-19 global pandemic. While there is no significant difference between the effects of the oil crisis on IBs versus CBs, the recent Covid-19 pandemic seems to have worsened the credit risk of IBs compared to CBs.

CREDIT RISK MANAGEMENT CONTROL ON SME SEGMENT: STUDY CASE OF XYZ BANK BRANCH SURABAYA

The study is conducted to explain the suitability of credit risk control management to minimize the non-performing loans at XYZ Bank Branch Surabaya as stipulated by the Basel Accord Committee in Financial Services Authority Regulation No. 18/PJOK.03/2016 about the Implementation of Risk Management for Commercial Banks. Based on the Basel III guidelines, credit risk management includes seven mechanisms. The seven mechanisms are the policy framework, credit risk rating framework, credit risk limits, credit risk modeling, credit risk mitigation, credit audits, and loan review mechanisms. The study uses a qualitative study by the case study approach. The results showed that the control of credit risk management implemented by Bank XYZ Surabaya Branch to minimize non-performing loans to individual debtors in the SME segment had not run effectively. It was primarily about the evaluation of credit risk, which led to the emergence of a new set of risks such as errors in credit risk evaluation, target market losses and ineffective allocation of loan funds, and inadequate strategies for collecting net credit to customers. The study also provides several recommendations for credit risk management as the strategy to reduce the risk of lending to individual debtors in the SME segment at XYZ Bank Branch Surabaya.

Credit Risk Evaluation in Banking and Lending Sectors Using Neural Network Model

This study presents the results of an experiment made with google colaboratory. The Google Colaboratory is also known as google colab it is a free online cloud-based Jupyter notebook environment that allows us to train our machine learning and deep learning models on CPUs, GPUs, and TPUs. We can determine the accuracy of the credit risk evaluation based on the dataset that was run in google colab using a neural network. The dataset has been thoroughly evaluated in creating a test harness for evaluating candidate models by calculating accuracy using k-fold cross-validation. When compared to a single train-test split, the k-fold cross-validation technique provides a reasonable general approximation of model performance. Based on the result, the correct number of rows was loaded, and through the one-hot encoding of the categorical input variables, it increased the number of input variables from 20 to 61. That suggests that the 13 categorical variables were encoded into a total of 54 columns. The result of the evaluation can help the banking and other financial sectors to assess a person before they are given a loan and if they can pay on time. This is a big help to reduce the losses of a company. Not only in the banking and other financial sectors as well as in lenders of goods.

Adaptively Promoting Diversity in a Novel Ensemble Method for Imbalanced Credit-Risk Evaluation

Ensemble learning techniques are widely applied to classification tasks such as credit-risk evaluation. As for most credit-risk evaluation scenarios in the real world, only imbalanced data are available for model construction, and the performance of ensemble models still needs to be improved. An ideal ensemble algorithm is supposed to improve diversity in an effective manner. Therefore, we provide an insight in considering an ensemble diversity-promotion method for imbalanced learning tasks. A novel ensemble structure is proposed, which combines self-adaptive optimization techniques and a diversity-promotion method (SA-DP Forest). Additional artificially constructed samples, generated by a fuzzy sampling method at each iteration, directly create diverse hypotheses and address the imbalanced classification problem while training the proposed model. Meanwhile, the self-adaptive optimization mechanism within the ensemble simultaneously balances the individual accuracy as the diversity increases. The results using the decision tree as a base classifier indicate that SA-DP Forest outperforms the comparative algorithms, as reflected by most evaluation metrics on three credit data sets and seven other imbalanced data sets. Our method is also more suitable for experimental data that are properly constructed with a series of artificial imbalance ratios on the original credit data set.

Optimization and Application of Random Forest Algorithm for Applied Mathematics Specialty

For unbalanced data classification, RF (Random forest) algorithm will cause problems such as poor classification performance and a large DT scale. With the advent of the era of big data, RF algorithms should have the ability to process large-scale data. Aiming at the problem that RF cannot handle unbalanced data well, this paper improves the feature selection method built in RF and proposes a new feature selection algorithm. On the basis of feature importance ranking, randomness is introduced to ensure the strength of each tree and reduce the correlation between trees. In the extended transform data set, the sensitivity of the RF model has exceeded 0.8, and that of other models has increased to about 0.65. The prediction accuracy of the centralized RF model for the company’s credit rating reached 100%, while the CART model misjudged companies C6 and C7, while the Logit model misjudged companies C3, C5, and C8. Experiments prove the extrapolation of the RF model and its excellent prediction ability. In the practical application of applied mathematics specialty, the RF optimization algorithm proposed in this study can well handle continuous variables and improve the classification accuracy of RF. This paper holds that the advantages of the RF algorithm in data processing and model performance will make it more widely used in the field of enterprise credit risk evaluation.

Credit Risk Assessment Modeling Method Based on Fuzzy Integral and SVM

With the development of financial globalization and financial market volatility, credit risk has become more prominent and serious, and how to establish an effective enterprise credit evaluation system and bank credit risk evaluation model, provide scientific quantitative decision-making basis for bank decision-making, reduce non-performing loans, and improve the quality of credit assets is a common research topic faced by domestic banks. At present, domestic banks have not been effective to establish risk prevention as the core of credit culture and long-term mechanism, the existence of nonperforming loans is still not fully resolved, new risks continue to appear, and there is a lack of a perfect and effective credit risk evaluation system. With the development of the Internet and financial institutions and the fusion, banks and financial institutions drastically increase the recorded data, and this provides a good prerequisite for the application of intelligent algorithms. In view of the shortcomings of BP neural network in the establishment of credit risk assessment model, such as poor promotion ability and long prediction time, and considering that support vector machine (SVM) can deal with some multi-classification problems, this paper introduces SVM method into the field of bank credit risk assessment and establishes an optimization model of credit risk assessment. This paper discusses the structure and algorithm principle of SVM classification method and proposes an integrated SVM based on fuzzy integral to solve this kind of problem. The results show that the algorithm can effectively improve the prediction accuracy, solve the problem of high computation cost, reduce the occupied memory space, improve the operation efficiency, shorten the training time, and provide a more reliable basis for the rapid and effective evaluation of bank credit risk. On the one hand, the research results expand the application of artificial intelligence technology in the field of economic research; the evaluation model can continuously and accurately measure credit risk is obtained, which provides the necessary basis for upgrading and optimizing credit decision-making, so it has high theoretical value and practical value.

Credit Risk Evaluation of Forest Farmers under Internet Crowdfunding Mode: The Case of China’s Collective Forest Regions

To effectively quantify and control the credit risk of forest farmers under internet crowdfunding mode, the combined weighting of norm grey correlation, the improved analytic hierarchy process and empirical mode decomposition method are proposed to measure the credit risk, and the interval rough number DEMATEL method is used to analyze the credit risk factors of forest farmers. Through the calculation of comprehensive influence degree, it is concluded that the degree of investor information asymmetry, the intensity of supervision, the degree of innovation and cooperation between funders and investors are the main credit risk factors of forest farmers under internet crowdfunding mode, and a credit risk control mechanism is constructed according to the main credit risk factors to effectively improve the risk management and control level of forest farmers.

A Path-Based Feature Selection Algorithm for Enterprise Credit Risk Evaluation.

In recent years, there has been increasing interest in exploring diversified features to measure small and medium-sized enterprises (SMEs) credit risk. Path-based features, revealing logical connections between SMEs, are widely adopted as informative feature kinds for causal inference in credit risk evaluation. Since there may exist thousands of feature paths to the target enterprise, to evaluate its credit risk, how to select the most informative path-based features becomes a challenging problem. To solve the problem, in this paper, we propose a novel method of feature selection, considering both similarity and importance on features' structured semantics as the factors of informativeness. With this, the proposed method can effectively rank both conventional and path-based features together. Furthermore, to improve the efficiency of the method, a heuristic algorithm is proposed to fast search for the candidate features. Through extensive experiments, we show our method performs competitively with other state-of-the-art selection methods.