Enterprise Credit Research Articles

Research on the causes and governance of the "thunderstorm" problem of real estate enterprises

The real estate industry is an indispensable pillar in the national economy. With the gradual development of the real estate industry, it is difficult to maintain the demand of the asset-heavy industry by virtue of its own capital accumulation, and most enterprises will choose debt management for better development. In recent years, there have been frequent "thunderstorms" in real estate enterprises in the market, and the trust of financial institutions and the general public in real estate developers has continued to decline. Therefore, it is very important to analyze the causes of "thunderstorm" in the real estate industry, and this paper analyzes the current situation of the real estate enterprise market and the causes of its "thunder storm", so as to propose prevention and control measures for enterprise credit risk, financing risk and business risk.

Does the owner’s caste affect access to credit for enterprises in India’s unorganized sector?

ABSTRACT Using a large publicly available dataset, we examine the extent of difficulty faced by marginalized caste entrepreneurs from the unorganized sector in India when trying to access formal credit for their small businesses. Contrary to the expectation that a perceived social vulnerability would translate to more difficult credit access, we find that entrepreneurs from marginalized castes are in fact more likely to obtain credit from the formal financial system. This result on the extensive margin of credit could be interpreted as the formal financial system’s positive bias towards the marginalized castes. However, when we focus on the actual loan amounts received, we find that all else being equal, entrepreneurs from marginalized castes receive significantly lower loan amounts. This result on intensive margin of credit could potentially be interpreted as evidence of negative discrimination by the formal financial system. Therefore, determining whether financial inclusion policies for marginalized caste entrepreneurs have been successful yields answers both ways, depending on whether we look at extensive or intensive margin of credit. This finding has serious implications for a good design, monitoring and evaluation of financial inclusion policies (such as mandatory lending within affirmative action programmes or priority sector lending) for borrowers from marginalized communities.

Design and Implementation of an Enterprise Credit Risk Assessment Model Based on Improved Fuzzy Neural Network

ABSTRACT In order to improve the effect of enterprise credit risk assessment, this paper combines the improved fuzzy neural network to construct the enterprise credit risk assessment model. The various jamming patterns that can threaten the credit data transmission system include various blocking jamming and tracking jamming. Moreover, this paper analyzes the bit error performance of the credit data transmission system against various interferences, and obtains the bit error curves against various interferences through program simulation. In addition, this paper builds a variable-speed credit data transmission system based on Simulink, analyzes the key technologies in variable-speed credit data transmission, and simulates the anti-interference performance of variable-speed credit data transmission technology. The simulation study verifies that the enterprise credit risk assessment model proposed in this paper has good risk assessment effect and risk response strategy effect.

Does the mobile phone affect social development? Evidence from Indonesian villages

This paper analyses the impact of Information and Communication Technology (ICT) on social development in Indonesian Villages. In this study, I use data from different waves of the Indonesian Village Potential Statistics (Potensi Desa) to determine whether mobile phone signal strength affects social development indicators. The results indicate that villages with a strong signal are statistically more likely to possess the proper infrastructure programs. Furthermore, mobile phones increase the availability of village libraries and access to credit for small and medium enterprises (SMEs). Using the plausibly exogenous variation of lightning strike intensity as the instrumental variable, this study suggests that higher mobile phone signal strength is positively associated with the policies implemented by the village head and leads to better social development. As the mechanisms, this study shows that mobile phones increase the likelihood of having collective action and civic engagement, increasing villagers’ political participation and use of telecommunication services, thus leading to an increase in village government’s accountability.

A Financial Management Platform Based on the Integration of Blockchain and Supply Chain.

Internet of Things (IoT) finance extends financial services to the whole physical commodity society with the help of IoT technology to realize financial automation and intelligence. However, the security of IoT finance still needs to be improved. Blockchain has the characteristics of decentralization, immutability, faster settlement, etc., and has been gradually applied to the field of IoT finance. Blockchain is also considered to be an effective way to resolve the problems of the traditional supply chain finance industry, such as the inability to transmit core enterprise credit, the failure of full-chain business information connections and the difficulty of clearing and settlement. Supply chain finance allows the strongest enterprise in the supply chain to apply for credit guarantee from the bank to obtain bank loans, and use the funds for circulation in the supply chain to ensure that each enterprise in the whole supply chain can obtain working capital to realize profits, so as to maximize common interests. In this paper, a financial management platform based on the integration of blockchain and supply chain has been designed and implemented. Blockchain is used to integrate supply chain finance to synchronize the bank account payment system, realize the automatic flow of funds, process supervision and automatically settle account periods based on smart contracts. The four functional modules of the system are designed using unified modeling language (UML), and the model view controller (MVC) architecture is selected as the main architecture of the system. The results of the system test show that the proposed platform can effectively improve the system security, and can use the information in the blockchain to provide multi-level financing services for enterprises in supply chain finance.

Enterprise credit risk portrait and evaluation from the perspective of the supply chain

AbstractThis paper aims to help financial institutions identify credit risk and reduce default losses more effectively by improving the accuracy and efficiency of the credit evaluation process using two methods. The paper first mines indicators that comprehensively describe the supply chain aspects of credit risk to obtain a precise credit risk portrait of the enterprise. The study then innovatively designs a random forest‐weighted naive Bayes (RF‐WNB) model that offers good interpretability and generalization qualities. The model forms an effective two‐stage process of enterprise credit risk evaluation that selects key features, quantifies the importance of each, and then evaluates credit risk. By applying empirical analysis to 1363 listed enterprises and performing one typical case analysis, we verified the effectiveness of the indicators and the two‐stage RF‐WNB model in evaluating enterprise credit risk.

RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

With the rapid development of the internet of things (IoT), electricity consumption data can be captured and recorded in the IoT cloud center. This provides a credible data source for enterprise credit scoring, which is one of the most vital elements during the financial decision-making process. Accordingly, this paper proposes to use deep learning to train an enterprise credit scoring model by inputting the electricity consumption data. Instead of predicting the credit rating, our method can generate an absolute credit score by a novel deep ranking model–ranking extreme gradient boosting net (rankXGB). To boost the performance, the rankXGB model combines several weak ranking models into a strong model. Due to the high computational cost and the vast amounts of data, we design an edge computing framework to reduce the latency of enterprise credit evaluation. Specially, we design a two-stage deep learning task architecture, including a cloud-based weak credit ranking and an edge-based credit score calculation. In the first stage, we send the electricity consumption data of the evaluated enterprise to the computing cloud server, where multiple weak-ranking networks are executed in parallel to produce multiple weak-ranking results. In the second stage, the edge device fuses multiple ranking results generated in the cloud server to produce a more reliable ranking result, which is used to calculate an absolute credit score by score normalization. The experiments demonstrate that our method can achieve accurate enterprise credit evaluation quickly.

Gendered Perspectives on Access to Credit for Micro, Small and Medium Enterprises in Kenya

Gendered Perspectives on Access to Credit for Micro, Small and Medium Enterprises in Kenya

Research on credit evaluation of enterprises in supply chain finance business

As a crucial link in the survival and development of enterprises nowadays, supply chain finance covers all aspects of enterprises and financial institutions. This study is dedicated to using techniques such as big data mining, mathematical modeling, industry standard definition and visualization analysis to apply the relationship between enterprises and each enterprise into a weighted directed network, determine credit rating weights using entropy weight method and TOPSIS, and monitor the information changes of enterprises in real time. With the relationship as the core, we collect and integrate enterprise credit information by means of feature extraction and graph construction, early warning risks, visualize and present complex business relationships, accurately supervise and serve the collateral and pledges by means of information technology, and timely grasp the operation status of goods and commodity price fluctuations staring the market, and visualize and present complex business relationships.

Research on the Enterprise Credit Risk Rating System in Supply Chain Finance Based on Jingdong

Research on the Enterprise Credit Risk Rating System in Supply Chain Finance Based on Jingdong

Influence of financial subsidies on innovation efficiency of start-up enterprises

The start-up period is the most difficult period for enterprises to survive and innovate. How will government subsidies affect the innovation efficiency of enterprises in the start-up period? And, is there any heterogeneity? The academic research is still relatively preliminary and controversial. This study adopts the cash flow method to screen a sample of start-ups from Chinese GEM, STAR Market and SME board-listed companies from 2010 to 2020 for empirical research. The study found that financial subsidies can generally promote the innovation efficiency of start-ups, but there is also significant heterogeneity by industry, region and whether the subsidies are sustainable. Financial subsidies generally have a signaling effect that facilitates startups to attract external capital, and this effect is most significant in the industrial sector. Overall, fiscal decentralization negatively regulates the effect of subsidies on the innovation efficiency of start-ups, especially in the public utilities and industrial sectors. The government subsidies are relatively insignificant to improve the innovation efficiency of start-up enterprises in non-first-tier cities, especially in small and medium-sized cities, and the multiplier effect is relatively small. The effect of non-sustainable subsidies is not significant because they cannot produce a long-term mechanism and thus are difficult to influence future expectations and decisions. The subsidy effect is not significant in other industries outside the industrial sector, and the lower the level of industry competition, the weaker the signaling effect of enterprise credit, the less conducive to its access to external innovation resources to enhance innovation efficiency through the signaling effect of government subsidies. Moreover, financial decentralization does not improve the innovation efficiency of government subsidies as expected, but weakens the innovation efficiency of fiscal subsidies for start-up enterprises in general. The study proposed that the government (especially the small and medium-sized city government) should optimize the business environment and financial capital allocation, change the supervision and management mechanism, improve the efficiency of capital utilization and reduce the crowding out effect. The government’s subsidy policy should be further standardized and refined.

Real estate assets, heterogeneous firms, and debt stability

By constructing a “financial accelerator” model that combines real estate assets and heterogenous enterprises, we use China’s housing price data to explore the effect of real estate assets on corporate debt and macroeconomic stability in the context of the Chinese economy. Based on the dynamic economic characteristics of the model, we find that the financial accelerator mechanism of housing prices further amplifies the various effects of housing price fluctuations when transmitted to the macroeconomic system, resulting in the size of corporate debt closely related to the economic boom–bust cycles. However, owing to the institutional factors, heterogenous firms have some differences in debt stability under the influence of asset price fluctuations. There is an obvious crowding-out effect of state-owned enterprise (SOE) credit on private-owned enterprise (POE) credit, and the existence of the implicit guarantee ratio of SOE credit will weaken the external financing constraints on the SOE and promote the expansion of SOE credit. Finally, the effect of fiscal and monetary policies on corporate debt stability is somewhat divergent, institutional factors can similarly weaken policy effects.

Risk prediction of enterprise credit financing using machine learning

For the credit financing risk of small, medium-sized and micro enterprises, a risk prediction model of enterprise credit financing management based on antagonistic neural network and least squares support vector machine is proposed. The data samples are processed combined with antagonistic neural network, and the risk prediction of enterprise credit financing is realized by using least squares support vector machine model. Compared with many classification models, the effectiveness of the least squares support vector machine model is verified. The results show that the accuracy rate of the least squares support vector machine model is 90.15%, the recall rate is 85.63%, and the prediction error rates of the model in default and non default are 6.48% and 3.09% respectively, which is better than BP neural network, random forest algorithm and Gaussian naive Bayesian algorithm. The least squares support vector machine model can effectively and accurately predict the enterprise credit financing risk, provide scientific and efficient risk early warning for the enterprise credit financing control, and provide technical support for preventing and reducing the occurrence of loan default.

Research on PPP Enterprise Credit Dynamic Prediction Model

The debt default risk of local government financing vehicles (LGFVs) has become a potential trigger for systemic financial risks. How to effectively prevent hidden debt risk has always been a hot issue in public-private partnership (PPP) financing management research. In recent years, machine learning has become more and more popular in the study of enterprise credit evaluation. However, most scholars only focus on the output of the model, and do not explain in detail the extent to which variables affect the model and the decision-making process of the model. In this paper, we aim to apply a better credit rating method to the key factors and analysis of LGFV’s default risk, and analyze the decision-making process of the model in a visual form. Firstly, this paper analyzes the financial data of LGFVs. Secondly, the XGBoost-logistic combination algorithm is introduced to integrate the typical characteristics of PPP projects and construct the credit evaluation model of LGFVs. Finally, we verify the feasibility of the model by K-fold cross validation and performance evaluation. The results show that: (1) net worth, total assets, operating income, and return on equity are the most critical factors affecting the credit risk of LGFVs, asset-liability ratio and tax revenue are also potentially important factors; (2) the XGBoost-logistic model can identify the key factors affecting the credit risk of LGFVs, and has better classification performance and predictive ability. (3) The influence of each characteristic variable on model decision can be quantified by the SHAP value, and the classification decision visualization of the model improves the interpretability of the model.

Finance in a More Globalized Economy

ABSTRACT This paper empirically examines whether globalization affects the level, volatility and structure of financial development as well as the competition and credit composition of the banking sector. Using dynamic panel estimation techniques, we find, in a sample of advanced and developing countries, that both trade and financial openness promote financial development, raise financial volatility, and result in a more market-based financial system, the effects that moderate with increased openness. It is also found that both trade and financial openness lead to a more competitive, less concentrated banking industry and a greater share of household credit relative to enterprise credit, the effects that dampen with increased openness.

JURIDICAL ANALYSIS OF BUSINESS CONVENIENCE FOR MICRO, SMALL AND MEDIUM ENTERPRISES (MSMEs) IN MEDAN CITY

The problems faced by MSME business actors are licensing and capital issues to continue their business in the midst of the COVID-19 pandemic. So special attention is needed for MSME actors from the Government during a pandemic. This research uses normative legal research and empirical legal research. Therefore, the data used include primary data and secondary data. Data were collected using library research and field research using interview methods. The results of the study indicate that the ease of doing business permits for MSME business actors according to the job creation law will be freed from business licensing fees and will provide cost relief for small businesses. The Government's policy is to provide social assistance to MSMEs affected by COVID-19 in the amount of 2.4 million Rupiah. Obstacles Faced First, there are obstacles in terms of licensing, the second is obstacles in distributing BPUM, and the third is the obstacles to providing capital credit for Micro, Small and Medium Enterprises (MSMEs).

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.

Analisis Rasio Penjaminan Kredit Usaha Rakyat Pada PT. Jamkrindo Pamekasan Di Masa Pandemi

Credit Guarantee is an activity of providing guarantees to debtors for credit or other facilities distributed to debtors due to non-fulfillment of the collateral requirements as determined by the creditor. The purpose of this study was to determine the credit guarantee ratio of PT. Jamkrindo KUP Pamekasan during the pandemic. The method applied is a descriptive approach. Types of data and data sources in this study is quantitative. The results of this study indicate that credit guarantees are needed in a loan, especially during the pandemic. With the credit guarantee (PT. Jamkrindo) it is hoped that micro, small or medium enterprises (SMEs) Credit guarantee is an activity of providing guarantees or services to creditors for credit, which is provided by the Indonesian general credit guarantee company (Jamkrindo) in collaboration with the government. This needs to be done to provide protection to creditors, if the debtor presents then the creditor still gets the right to his receivables in order to overcome failure to fulfill obligations and repay bank credit.

The Evaluation on the Credit Risk of Enterprises with the CNN-LSTM-ATT Model.

Credit evaluation is a difficult problem in the process of financing and loan for small and medium-sized enterprises. Due to the high dimension and nonlinearity of enterprise behavior data, traditional logistic regression (LR), random forest (RF), and other methods, when the feature space is very large, it is easy to show low accuracy and lack of robustness. However, recurrent neural network (RNN) will have a serious gradient disappearance problem under long sequence training. This paper proposes a compound neural network model based on the attention mechanism to meet the needs of enterprise credit evaluation. The convolutional neural network (CNN) and the long short-term memory (LSTM) network were used to establish the model, using soft attention, the gradient propagates back to other parts of the model through the attention mechanism module. In the multimodel comparison experiment and three different enterprise data experiments, the CNN-LSTM-ATT model proposed in this paper is superior to the traditional models LR, RF, CNN, LSTM, and CNN-LSTM in most cases. The experimental results under multimodel comparison reflect the higher accuracy of the model, and the group test reflects the higher robustness of the model.