Financial Credit Risk Research Articles

Financial credit risk assessment of online supply chain in construction industry with a hybrid model chain

Under the influence of COVID-19, although upstream small- and medium-sized enterprises (SMEs) in construction industry chain suffer from high operating costs and tight cash flow problems, their financing demands are even stronger. As an electronic and platform-based comprehensive service, online supply chain finance can ease the financing problems of SMEs in the construction industry. However, it has become an important issue for financial institutions to effectively assess the credit risk in the process of online supply chain financing. In this paper, an online supply chain risk assessment method, which is based on a hybrid model chain, including eXtreme Gradient Boosting (XGBoost), Synthetic Minority Oversampling TEchnique for Nominal and Continuous (SMOTENC), and Random Forest (RF), is proposed to identify and control the credit risk of financial institutions. Specifically, we establish the financial credit risk assessment system with respect to the characteristics of financing enterprises in the construction industry supply chain, including the status of financing enterprises, the status of core enterprises, the operating status of the supply chain, and the status of assets under financing. On the basis of the system, the best index number of the assessment system and the minority samples are obtained by the XGBoost algorithm and the SMOTENC algorithm, respectively. The classification method based on RF is applied to judge the credit risk of financing enterprises in the supply chain of construction industry. In the simulation stage, we take upstream SMEs in the supply chain of construction industry in China as an example for empirical analysis to validate the effectiveness of our proposed method. The credit risk assessment method proposed in this paper has better performance than the commonly used ones in the academic field with an average improvement on assessment accuracy for 6.39% and an average increase of Area Under Curve for 6.95%. Our study provides meaningful exploration on the fund monitoring system of the financing service platform to improve financing efficiency and risk management level.

Risk Measurement of the Financial Credit Industry Driven by Data

The risk measurement of financial credit industry is an important research issue in the field of financial risk assessment. The design of financial credit risk measurement algorithm can help investors avoid greater risks and obtain higher returns, so as to promote the benign development of financial credit industry. Based on the combined deep learning algorithm, this paper studies the risk measurement of financial and credit industry, and proposes a fusion algorithm of deep auto-encoder (DAE) and Long Short-Term Memory (LSTM) network. The algorithm recombines the value of fixed features by using the unsupervised mechanism of DAE, and extracts non fixed features for measurement combined with the memory characteristics of LSTM network. The experimental results show that: compared with single generalized regression neural network and LSTM network, the average accuracy of DAE-LSTM algorithm is improved by about 6.49% and 3.25% respectively, which has a better application effect in credit risk measurement.

Improved AHP Model and Neural Network for Consumer Finance Credit Risk Assessment

With the rapid expansion of the consumer financial market, the credit risk problem in borrowing has become increasingly prominent. Based on the analytic hierarchy process (AHP) and the long short-term memory (LSTM) model, this paper evaluates individual credit risk through the improved AHP and the optimized LSTM model. Firstly, the characteristic information is extracted, and the financial credit risk assessment index system structure is established. The data are input into the AHP-LSTM neural network, and the index data are fused with the AHP so as to obtain the risk level and serve as the expected output of the LSTM neural network. The results of the prewarning model after training can be used for financial credit risk assessment and prewarning. Based on LendingClub and PPDAI data sets, the experiment uses the AHP-LSTM model to classify and predict and compares it with other classification methods. Experimental results show that the performance of this method is superior to other comparison methods in both data sets, especially in the case of unbalanced data sets.

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.

Research on Financial Credit Risk Prediction under the Internet Background

As a new type of financial business model linked to Internet technology, Internet finance can solve the financing difficulties of individuals and small and medium-sized enterprises to a certain extent. With the continuous development of Internet technology, the data sources of Internet finance are more extensive and the scale of data is getting bigger and bigger. This paper combines two two-step sub-sampling algorithms with logistic regression model, and analyzes the prediction effect of Internet financial credit risk through the results of numerical simulation and empirical analysis. The logistic regression model based on the two-step subsampling algorithm maintains high accuracy and saves time significantly. Therefore, the regression model based on the two-step subsampling algorithm can make good predictions on the credit risk of Internet finance in both high-dimensional and low-dimensional Internet financial credit data.

Islamic banks credit risk performance for home financing: Before and during Covid-19 pandemic

Purpose ― This study aims to assess the home financing credit risk performed by Islamic banks in Indonesia. Methods ― A panel dynamic analysis is adopted to measure the bad loan performance before and during the Covid-19 pandemic. The observation period started from January 2016 to September 2020 with 1,881 observation periods of monthly panel data from the province level. Findings ― The study finds a difference in bad loan performance before and during the Covid-19 pandemic. Before this pandemic, inflation has a positive and significant influence on non-performing financing in real estate, rental business, and company service. However, during the Covid-19 pandemic, a substantial and positive effect of inflation is found on the bad loan for personal flat and apartment ownership. On the other hand, a significant and negative impact of inflation is found on the bad home loan for personal business shop ownership. Implication ― This analysis could trigger the government to provide financial assistance for those affected by the Covid-19 crisis. In addition to that, an Islamic bank is also expected to give financing allowances for them by providing an option of debt restructuration and rescheduling. Originality ― This paper analyses the Islamic bank’s credit risk performance for home financing before and during the Covid-19 pandemic. This issue has not been presented in the literature to the best of our knowledge.

Multi‐criteria probabilistic dual hesitant fuzzy group decision making for supply chain finance credit risk assessments

AbstractSupply chain finance is important for resolving small and medium‐sized enterprise financing difficulties and optimizing supply chain capital flows. Therefore, supply chain finance credit risk assessments are key financing indicators for decision makers. However, most current supply chain finance credit risk assessment research has been based on traditional and subjective methods and has focused less on the actual decision‐making requirements. The TODIM method in a fuzzy environment has proven to be a useful tool for multi‐criteria decision‐making as it can better reflect the DMs' psychological characteristics. As probabilistic dual hesitant fuzzy sets can contain more original evaluation information and more comprehensively reflect the DM uncertainties, this paper first proposes a Hamming distance measure in a probabilistic dual hesitant fuzzy environment to compare the relationships between two probabilistic dual hesitant fuzzy elements and prove some of the properties. Then, to fully consider the DMs' risk preferences, a model is proposed to evaluate supply chain finance credit risk that extends the TODIM method to a probabilistic dual hesitant fuzzy environment based on cumulative prospect theory and the proposed Hamming distance. Finally, using improved credit risk assessment criteria, a practical case from Ping An Bank is given to demonstrate the proposed model's practicality, and a comparison given with the classical TODIM and the probabilistic hesitant fuzzy TODIM method to illustrate the effectiveness of the extended method.

Supply Chain Financial Default Risk Early Warning System Based on Particle Swarm Optimization Algorithm

With the advancement of the linkage between financial markets, the probability of credit risk infection is also increasing. Traditional financing methods, which mostly relied on corporate credit to give credit to the whole supply chain, have been replaced by supply chain finance. This paper studies the supply chain financial credit risk through the logistic model and chooses the financial data and supply chain financial operation indicators of relevant listed companies from 2014 to 2016 for analysis. Because not all of companies can find the bad debt rate of accounts receivable from 2014 to 2016, and some agricultural listed companies only have one or two years of relevant data, this paper creates an unbalanced panel data with 91 sample sizes, which is larger than previous studies. Binary logistic regression and principal component analysis are mainly used to accurately calculate the compliance probability of cooperative customers in agricultural supply chain financial products. Unlike the existing literature, which mainly uses s.t to define whether an enterprise defaults, this paper uses Z value to define the default risk of listed companies in agricultural supply chain finance. In terms of the default risk value of the company, Z value not only has high accurate value but also has advantages in accurate prediction, which effectively complements and improves the existing research on supply chain finance.

Study on Enterprise Financial Risk Prevention and Early Warning System Based on Blockchain Technology

To address the challenges of financing SMEs, breaking the limitations of traditional financing models, a blockchain-based financial risk prevention and early warning system for businesses has been proposed and an in-depth study of the system has been undertaken. As a scientific and technological means to strengthen the disclosure and sharing of information such as credit records and risk early warning, The key to getting financial institutions to lend to SMEs is to avoid financial credit risk in the value chain. Considering SMEs located in supply chains as an object of study, using the logical relationships between block chains and financial credit risk management chains, three principles of supply chain management are proposed by analyzing the logical relationships between supply chains and financial credit control chains. Chain using blockchain technology, namely: the principle of recognizing the trust of major enterprises, the principle of sharing information about the financing process and the principle of general cooperation for successful financing. Based on this principle, an evolutionary game model is used to analyze the evolutionary path of endorsement fiduciary relationships between major enterprises and SMEs, the influencers of evolutionary equilibrium, the evolutionary path of cooperation between commercial banks and SMEs in financing, the evolutionary path under the control of third-party institutions, and influencers. The research shows that the financing activities of commercial banks and small and medium-sized enterprises are based on the trust endorsement of core enterprises in the supply chain, and the intervention of third-party regulators will help to promote the consistent cooperation and information sharing of all participants in the supply chain, so as to promote the smooth completion of supply chain financing.

PENGARUH RISIKO KEUANGAN TERHADAPA KINERJA KEUANGAN PADA PERBANKAN INDONESIA

The reserarch aims to determine and test the effect of financial risk on the financial performace of banks (the case study on banks listed on te Indonesian stock exchange). The indicator usud in this reserach is the Return on Assets (ROA) for Financial Performance, Operational Efficiency Ratio (BOPO) for Operational Risk, Capital Adequacy Ratio (CAR) for Capital Risk, Loan to Deposit Ratio (LDR) for Liquidity Risk, dan Non Performing Loan (NPL) for Credit Risk. This study took a sample of banking compies listed in Indonesian Stock exchange from 2016-2020 and the sampling technique using puposive sampling technique. The sample obtained is 6 sample company. The analysis used in this study is analysis panel data regression. The parcial test show that operation risk has negative but not significant effect on financial performance, capital risk has a postive and significant effect on financial performance, liquidity risk has negative but not significant effect on financial performance and credit risk has negative and significant effect on financial performance. Meanwhile, the simultaneous show that the financial risk has significant effect on financial performance.
 Keywords : Financial Performance, Fianancial Risk, Operation Risk, Capital Risk, Liquidity Risk, And Credit Risk

Terrorist attacks and bank financial stability: evidence from MENA economies

This study examines the impact of terrorism on bank stability, represented by bank risk and financial performance. We consider banks from 14 countries located in the Middle East and North Africa region for the period 2010–2018 using both the three-stage least-square and the generalised method of moments. The results provide strong evidence that banks located in countries with high exposure to terrorist attack exhibit low financial stability, due to high bank risk (i.e., high credit and insolvency risk). However, these banks show high financial performance (i.e., high profitability and cost efficiency), on average. Our results also show differential impacts on bank stability for countries marked as more (less) exposed to risk of attacks. For banks located in high-income-generating countries, we find that exposure to terrorism is associated with low financial performance and high credit risk, which is the opposite case for low-income-generating countries. Our results also indicate high systemic risk for listed banks operating under high terrorism risk exposure.

Data Ecology and Accurate Portrait: Optimization of Credit Risk System for SMEs in Supply Chain Finance Based on Big Data Technology

Big data technology can collect data, store it, mine it, and create an accurate portrait. It can assist financial institutions in resolving information asymmetry between banks and enterprises, as well as lowering the likelihood of default of small and medium-sized financing enterprises (SMEs). The credit risk system for SMEs in supply chain finance can realize “visualization” management of credit risk with the help of open public data in government affairs, collaborative development of various technologies, and the establishment of an ecological platform with transparent and accurate data portraits. The platform with accurate risk warning capability can reduce the risk monitoring cost and improve the risk management efficiency of financial institutions. The core enterprises are more willing to grant credit to SMEs through the big data technology supervision platform, which significantly improves the financing efficiency of SMEs. Moreover, a better financing credit circumstance also could improve transaction efficiency of enterprises and deeply connect the business relationship between enterprises. The main conclusion of this research: big data technology has a significant impact on supply chain in the digital economy era. Firstly, big data technology can identify credit risks accurately, which narrows the "information gap" between financial institutions and supply chain financing enterprises, and lower the likelihood of credit default. Secondly, financial institutions can allocate funds accurately based on the “visualization” information provided by the big data platform, and strengthen supervision of the use of funds. Lastly, the supply chain finance credit risk supervision system based on big data technology promotes the deep integration of big data and real economy. Therefore, in order to ensure the sustainable development of supply chain finance and financing risk management, it is necessary to create a digital ecosystem of supply chain finance with supply chain finance control tower as its core, as well as a supply chain finance credit risk control system based on big data in the context of the continuous development of big data technology.

An Enterprise financial credit risk measurement method based on differential evolution algorithm

An Enterprise financial credit risk measurement method based on differential evolution algorithm

Credit Risk Assessment of Small and Medium‐Sized Enterprises under the Financial Model of Online Supply Chain

The rapid development of Internet technology meant that online supply chain finance has become an important source of small‐ and medium‐sized enterprise (SME) finance. From a review of associated studies, this study constructed an online supply chain financial credit risk evaluation index system that had eleven level 2 indicators and 28 level 3 indicators for the four dimensions of financing enterprise qualification, core enterprise qualification, supply chain operations, and the macroenvironment. To assess the viability of this indicator system, data on 368 SMEs in four industries, clothing, home appliances, pharmaceutical, and construction, were selected as research samples. An online supply chain financial credit risk evaluation model for the different industries was then constructed using principal component analysis (PCA) and logistic regression methods. It was concluded that when evaluating online supply chain financial credit risk, it is necessary to focus on the solvency, profitability, and asset structure efficiencies of the financing enterprises. Due to the different production and operating characteristics of industries, significant variations in the degree and direction of the credit risk factors between industries were found; therefore, evaluating credit risks by industry significantly improved the accuracy of the model’s credit risk predictions. For example, home appliance SMEs need to pay greater attention to their inventory turnover and construction industry SMEs should pay greater attention to their sales growth rate, return on common stockholders’ equity, and GDP growth rate. Based on these results, some suggestions for commercial banks, supply chain core enterprises, and SMEs are given to improve supply chain financing. The conclusion of this study enriches the related research on credit risk assessment of SMEs and also provides decision support for improving SMEs to prevent credit risk.

The Internet Creates Increased Credit Default Risk for the Financial Industry

The advent of the Internet has changed modern life, creating new opportunities in areas such as work, study and leisure. However, as a result of this new technology, many problems have also emerged at the same time. The purpose of this paper is to examine an important issue related to the Internet within the financial industry and the methods to solve it. This study shows that credit risk is one of the biggest risks of internet finance as the internet is a virtual trading platform. The increased default risk caused by information asymmetry that the Internet brings to the financial industry has two main consequences, namely the inability to recover the borrowed funds and the possible increase in the bankruptcy rate. In response to this risk, the establishment of an internet-based financial credit risk prevention and control system and enhanced dissemination and popularization of internet financial knowledge to the public can appropriately mitigate the higher credit default risk brought by the Internet to the financial industry.

Problems and Major risks in the Banking Sector of Azerbaijan

Aim. The presented study aims to perform a comprehensive and in-depth analysis of problems and risks in the banking sector of Azerbaijan and to develop recommendations for improving the efficiency and reliability of its functioning in accordance with national interests.Tasks. The author shows the key problems and risks in the country’s banking sector, focusing on dollarization as the main risk; investigates the content and causes of dollarization; examines new trends, particularly the impact of the COVID-19 pandemic on the banking sector of Azerbaijan.Methods. A systems approach to identifying the major factors affecting the development of the banking sector in Azerbaijan serves as the methodological basis of this study. The author also uses the methods of scientific analysis, such as the statistical method, comparative, fundamental, and functional analysis, and expert assessment. The study reflects the ideas set forth in the scientific works of economists on the problems of the institutional development of the banking system in the country under consideration.Results. According to the results of the study, the patterns and current problems in the development of the banking sector of Azerbaijan after the devaluation of 2015 are identified. Its major risks and current state are analyzed, and the reasons for the dollarization of the economy as the main channel of risk for the banking sector of Azerbaijan are determined. In addition, the impact of the COVID-19 pandemic on this sector is analyzed.Conclusions. Positive and negative aspects are identified by performing a risk analysis of the banking sector of Azerbaijan. On the positive side, there are large strategic monetary reserves, a relatively low level of public debt, a single authority for regulating and supervising financial markets, basic risk management elements, and full deposit insurance. The vulnerabilities of the banking sector of Azerbaijan include fragile capital positions of the banking system, high credit risks of banks, low capitalization of banks, lack of legal mechanisms for regulating and selling non-performing assets, lack of effective tools for the rehabilitation and restructuring of banks, weak corporate governance in the financial services sector, low financial literacy, increased monetary and credit risks in the banking sector.

Analysis of Internet Financial Risk Control Model Based on Machine Learning Algorithms

On the basis of traditional credit risk control, this paper proposes the demand and direction of a new credit risk control strategy based on machine learning and relying on big data. First, on the basis of introducing the basic algorithmic principles of machine learning, we give reasons for choosing machine learning models and build a machine learning-based Internet consumer finance credit risk control strategy model to provide theoretical support for the empirical analysis later. Second, we take the test data of Internet consumer finance S company as the research sample and carry out empirical analysis according to the machine learning-based Internet consumer finance credit risk control strategy model. The comparison of the training results is based on the comprehensive consideration of training time, validation set accuracy, TPR evaluation indicators, and interpretability of the results; it verifies the advantages of the machine learning model in screening the key influencing factors that cause the overdue performance of credit customers. According to the optimized credit risk control strategy, corresponding strategy suggestions are provided for the credit risk control of S company. The research results show that the prediction effect of the classification model based on traditional linear regression is generally lower than that of the model based on the classification algorithm based on machine learning, and there is a complex nonlinear relationship between platform default and its related influencing factors. The accuracy of classification and early warning results of the random forest algorithm is relatively high, and the detection rate of the decision tree model is relatively high, but the cost is also the highest. In addition, the accuracy of the four types of early warning models is relatively stable, reaching an average of 80%. This paper proposes a machine learning-based Internet consumer finance credit risk control strategy model. Its system, timeliness, and risk prediction capabilities provide new ideas and suggestions for Internet consumer finance companies to design risk control strategies.

Optimization Method of Supply Chain Financial Credit Risk System based on Computer-Aided Technology

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

Exploration of Financial Market Credit Scoring and Risk Management and Prediction Using Deep Learning and Bionic Algorithm

The purpose is to effectively manage the financial market, comprehensive assess personal credit, reduce the risk of financial enterprises. Given the systemic risk problem caused by the lack of credit scoring in the existing financial market, a credit scoring model is put forward based on the deep learning network. The proposed model uses RNN (Recurrent Neural Network) and BRNN (Bidirectional Recurrent Neural Network) to avoid the limitations of shallow models. Afterward, to optimize path analysis, bionic optimization algorithms are introduced, and an integrated deep learning model is proposed. Finally, a financial credit risk management system using the integrated deep learning model is proposed. The probability of default or overdue customers is predicted through verification on three real credit data sets, thus realizing the credit risk management for credit customers.

Analysis of Influencing Factors of Financial Market Volatility Based on Cluster Analysis

The financial market has certain volatility in its stability due to the influence of many factors. In the analysis of the financial market, it is necessary to combine scientific and effective intelligent algorithms. Based on the cluster analysis algorithm, this paper analyzes the factors affecting financial market volatility. Moreover, this paper conducts a more in-depth study and summary of the fuzzy C-means clustering algorithm, derives the analysis and solution process of the improved algorithm in detail, and gives the steps to improve the clustering algorithm. In addition, this paper constructs an analysis model of financial market volatility influencing factors based on cluster analysis and establishes a supply chain financial credit risk evaluation index system that includes the quality of corporate managers, corporate indicators, and asset structure. Finally, this paper verifies the performance of the algorithm model of this paper through experimental research. It can be seen from the experimental research results that the methods and models proposed in this paper have certain effects.