Construction of BDS Spatiotemporal Information Agricultural Product Digital Credit System

Spatiotemporal information,positioning and navigation services have become important elements of new type infrastructure.The rapid development of global digital trade provides a large-scale application scenario for the use of Beidou Navigation Satellite System(BDS)spatiotemporal information to support the certification of origin of agricultural products.The BDS spatiotemporal information agricultural product digital credit system uses such modules as BDS,spatiotemporal information collection,spatiotemporal coding,and spatiotemporal blockchain.It incorporates multi-level joint supervision mechanisms such as government,associations,and users.Starting from the initial production link of agricultural products,it realizes the correspondence and locking of online and offline products,effectively improves the integrity and credibility of information in the production process,finished product quality and circulation process of products,effectively manages the green production and anti-channel conflicts of producers,and provides credible information for consumers,thus realizing the digital credit certification of products.The successful practice of characteristic agricultural products in Yunnan Province has verified the application ability of the BDS spatiotemporal information agricultural product digital credit system.This system has played a significant role in promoting the online and offline locking,credible information,effective supervision and high quality and high price of characteristic agricultural products from the field.The BDS provides services for global digital trade and contributes to the further enhancement of the global application scale of GNSS.

Analysis on Internet Financial Business and Construction of Credit System

Internet finance is a new emerging financial model,using the Internet as a platform,big data and cloud computing as the basis.Supply Chain Finance is the easiest way to enter Internet finance.The thirdparty companies or institutions can invest in Internet financial companies by integrating their industrial chain practices into designing the financial products to reduce credit costs and improve safety.At the same time,it will increase mobile Internet,big data and operational services.Also,it can make full use of the Internet financial platform to provide value-added services for higher and lower enterprise and consolidate the core status of the company in the industrial chain.However,an important issue that needs to be concerned during developing Supply Chain Finance is the construction of a system for credit evaluation.Due to the lack of a unified credit evaluation system,the development of the existing Supply Chain Financial companies suffers from difficulties.Many newly launched companies have difficulties operating due to the lack of a credit evaluation system.Therefore,proper and effective credit indicators are essential for the development of enterprises under Internet finance.From the micro perspective,it is conducive for enterprises to improve their credit under the constraints of indicators,and it can solve the problem of capital raising;from the macro perspective,it is conducive to the standardized development of China’s Internet finance and promotes the comprehensive economic development.Based on this,analyzing the model of Internet financial business and developing an enterprise’s credit index system is beneficial to the development of China’s Internet finance.

Personnel Cultivation Program for Innovative and Entrepreneurial Biopharmaceutical Discipline under the Credit System

Biopharmaceutical discipline is an interdisciplinary subject with strong comprehensiveness and wide coverage. Under the background of credit system,it is an important task for application-oriented undergraduate colleges and universities to optimize the cultivation program for innovative and entrepreneurial bio-pharmaceutical professionals. According to the characteristics of biopharmaceutical discipline,Binzhou University biopharmaceutical teaching and research office,based on the social demand for biopharmaceutical discipline talents,defined the principle of optimizing the cultivation of innovative and entrepreneurial biopharmaceutical discipline talents,and constructed the cultivation program of innovative and entrepreneurial biopharmaceutical discipline talents under the credit system. The development of this cultivation program is expected to build a new mode for cultivating high-level biopharmaceutical professionals with strong innovative spirit and entrepreneurial potential.

Default or profit scoring credit systems?Evidence from European and US peer-to-peer lending markets

For the emerging peer-to-peer(P2P)lending markets to survive,they need to employ credit-risk management practices such that an investor base is profitable in the long run.Traditionally,credit-risk management relies on credit scoring that predicts loans’probability of default.In this paper,we use a profit scoring approach that is based on modeling the annualized adjusted internal rate of returns of loans.To validate our profit scoring models with traditional credit scoring models,we use data from a European P2P lending market,Bondora,and also a random sample of loans from the Lending Club P2P lending market.We compare the out-of-sample accuracy and profitability of the credit and profit scoring models within several classes of statistical and machine learning models including the following:logistic and linear regression,lasso,ridge,elastic net,random forest,and neural networks.We found that our approach outperforms standard credit scoring models for Lending Club and Bondora loans.More specifically,as opposed to credit scoring models,returns across all loans are 24.0%(Bondora)and 15.5%(Lending Club)higher,whereas accuracy is 6.7%(Bondora)and 3.1%(Lending Club)higher for the proposed profit scoring models.Moreover,our results are not driven by manual selection as profit scoring models suggest investing in more loans.Finally,even if we consider data sampling bias,we found that the set of superior models consists almost exclusively of profit scoring models.Thus,our results contribute to the literature by suggesting a paradigm shift in modeling credit-risk in the P2P market to prefer profit as opposed to credit-risk scoring models.

Credit Systems for the Rural Poorin the Economic Transition of China:Institutions, Outreach, and Policy Options

Siniceundertakingmarket-orientedreformsinthelate1970s,Chinahasenjoyedrapideconomicgrowthandasubstantialreductioninpoverty.Asaresultthecountry’sfinancialsystemisunderincreasingpressuretoadjusttotheneweconomicoutlook.RuralChina,forinstance,lacksacompre...

The Demographic Impact on Credit Scores＂ Evidence From Statistical Methods and Geographic Information Systems （GIS） Mapping

Average credit scores for people in the United States （US） differ from state to state. Some states have high, and some states have low average credit scores. Since lenders and employers use credit scores to make loan and employment decisions, people living in states where average credit scores are high should experience the benefits of living where credit scores tend to allow more favorable loan and employment decisions. Although credit scores are the direct result of credit histories, credit histories may be impacted by demographic factors. If the demographic factors that impact credit histories are identified, ways to improve credit scores are likely to be discovered and available to people and state government policymakers. This study looks for demographic factors to indirectly explain the average credit scores for people living in each state of the US. The methodology includes statistical analyses and geographic information systems （GIS） mapping. Statistical analyses provide evidence to suggest that state average credit scores are explained by the demographic factors of education, family, income, and health. GIS mapping reveals clusters of states with similar demographics and credit scores.

Methods for Increasing Creditability of Anomaly Detection System

Based on Bayes' theorem we point out that the false positive rate must be lower than the intrusion base rate in order to make the Alarm Credibility Probability of the intrusion detection system exceed 50%. We present the methods that have been used in our developing intrusion detection system AIIDS (artificial immune intrusion detection systems) to increase the creditability of anomaly detection system. These methods include increasing the regularities of the system call trace by use of Hidden Markov Model (HMM), making every antibody or detector has finite lifetime, offering the detector a co-stimulate signal to illustrate whether there is damage in the system according to the integrity, confidentiality, or availability of the system resource.

Social Credit System

China will take enterprises credit data into consideration during the approval process of initial public offerings in an effort to develop a national social credit system, Ch/na Daily reported on December 4.Enterprises with past records of untrustworthiness will be strictly examined by securities regulators when applying for initial public offerings and the issuance of convertible bonds, according to a memorandum of understanding (Moll) issued by the China Securities Regulatory Commission (CSRC) and the National Development and Reform Commission (NDRC).

Credit Default Swaps （CDSs） and Systemic Risks

The use of credit default swaps （CDSs） has become increasingly popular over time. Between 2002 and 2007, gross notional amounts outstanding grew from below S2 trillion to nearly S60 trillion. The recent crisis has revealed several shortcomings in CDS market practices and structure. In addition, management of counterparty risk has proved insufficient, as has in some instances the settlement of contracts following a credit event. However, past problems should not distract from the potential benefits of these instruments. In particular, CDSs help complete markets, as they provide an effective means to hedge and trade credit risk. CDSs allow financial institutions to better manage their exposures, and investors benefit from an enhanced investment universe. The purpose of this paper is to present a complete and practical exposition of the CDS market and to explore how the development of the CDS market has played an important role in the credit risk markets. Currently, the CDS market is transforming into a more stable system. Various measures are being put in place to help enhance market transparency and mitigate operational and systemic risk. In particular, central counterparties have started to operate, which will eventually lead to an improved management of individual as well as system-wide risks.

The Development of an Alternative Method for the Sovereign Credit Rating System Based on Adaptive Neuro-Fuzzy Inference System

The main purpose of this article is to determine the factors affecting credit rating and to develop the credit rating system based on statistical methods, fuzzy logic and artificial neural network. Variables used in this study were determined by the literature review and then the number of them was reduced by using stepwise regression analysis. Resulting variables were used as independent variables in the logistic model and as input variables for ANN and ANFIS model. After evaluating the models and comparing with each other, the ANFIS model was chosen as the best model to forecast credit rating. Rating determination was made for the countries that haven’t had a credit rating. Consequently, the ANFIS model made consistent, reliable and successful rating forecasts for the countries.

Effects of formal credit on pastoral household expense: Evidence from the Qinghai-Xizang Plateau of China

Formal credit is critical in agricultural production,allowing more expenditure and productive input,thereby improving farmers'welfare.In pastoral China,formal financial institutions are gradually increasing.However,a limited understanding remains of how formal credit affects herders'household expenses.Based on a survey of 544 herders from the Qinghai-Xizang Plateau of China,this study adopted the propensity score matching approach to identify the effect of formal credit on herders'total household expenses,daily expenses,and productive expenses.The results found that average age,grassland mortgage,and other variables significantly affected herders'participation in formal credit.Formal credit could significantly improve household expenses,especially productive expenses.A heterogeneity analysis showed that formal credit had a greater impact on the household total expense for those at higher levels of wealth;however,it significantly affected the productive expense of herders at lower wealth levels.Moreover,the mediating effect indicated that formal credit could affect herders'household income,thus influencing their household expenses.Finally,this study suggests that policies should improve herders'accessibility to formal credit.

Pricing Catastrophe Options with Credit Risk in a Regime-Switching Model

In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.

Application of a Multi-Agent System (MAS) to Rational Credit Rating

A Multi-Agent System （ MAS ） is a promising approach to build complex system. This paper introduces the research of the Inner-Enterprise Credit Rating MAS （ IECRMAS）. To raise the rating accuracy, we not only consider the rating-target＇s information, but also focus on the evaluators＇ feature information and propose the rational rating-group formation algorithm based on an anti-bias measurement of the group. We also propose the rational rating individual, which consists of the evaluator and the assistant rating agent. A rational group formation protocol is designed to coordinate autonomous agents to perform the rating job.

Evolutionary analysis of green credit and automobile enterprises under the mechanism of dynamic reward and punishment based on government regulation

To explore the green development of automobile enterprises and promote the achievement of the“dual carbon”target,based on the bounded rationality assumptions,this study constructed a tripartite evolutionary game model of gov-ernment,commercial banks,and automobile enterprises;introduced a dynamic reward and punishment mechanism;and analyzed the development process of the three parties’strategic behavior under the static and dynamic reward and punish-ment mechanism.Vensim PLE was used for numerical simulation analysis.Our results indicate that the system could not reach a stable state under the static reward and punishment mechanism.A dynamic reward and punishment mechanism can effectively improve the system stability and better fit real situations.Under the dynamic reward and punishment mechan-ism,an increase in the initial probabilities of the three parties can promote the system stability,and the government can im-plement effective supervision by adjusting the upper limit of the reward and punishment intensity.Finally,the implementa-tion of green credit by commercial banks plays a significant role in promoting the green development of automobile enter-prises.

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

Establishment of Comprehensive Evaluation Indicator System for Sustainable Development of Microcredit and Empirical Analysis

We establish the comprehensive evaluation indicator system for sustainable development of micro-credit,constituted by the profitability ratio,financial constitution ratio,loan quality ratio and operating efficiency ratio.Taking the case of micro-credit of China Foundation for Poverty Alleviation,we measure the current situation of sustainable development of micro-credit.As far as we are concerned,the composite index of sustainable development of micro-credit shows the growth trend on the whole.Finally,in order to further improve the sustainable development of microcredit,provide farmers with better financial services and enable more farmers to benefit from micro-credit,we put forth the following recommendations:strengthening technological innovation to reduce costs and improve operating efficiency and profitability of the micro-credit;strengthening risk control to improve the quality of loans;providing all-around non-financial services to lay the foundation for the sustainable development of microcredit;setting an appropriate level of interest rates to improve the profitability of the institutions.

A Probe into the Talent Training Mode of Vocational Colleges with"l+X"Certificate System based on Credit Bank

Based on credit bank,the"1+X"certificate system is a"flyover"for learners to interchange between the academic and vocational education.It is a significant innovation to promote high-quality vocational education development and enhance the influence of national vocational education.Vocational colleges can implement"1+X"certificate system via the credit bank and in turn,the popularization of the"1+X"certificate system would enrich and improve the former.China's development of the credit bank and the existing research on the"1+X"certificate system are analyzed to look for a way in the collaboration of credit bank and the T+X"certificate system.The firm collaboration as well as the connection and optimization between government,schools,and enterprises as one body form a new talent training mode with the"1+X"certificate system that integrates academy and certificates as well as involving both,schools and enterprises according to China's credit bank concept.

Research on Early Warning of Banking Crises from the Perspective of Credit Structures

The relationship between credit expansion and banking crises is complex and cannot be fully explained by total credit alone.A systematic analysis of the relationship between the amount and structure of total credit and banking crises is important for an objective prediction of the influence of potential financial risks.This paper,drawing on data from 15 selected countries,delves into the power of credit indicators in the early warning of banking crises from the perspectives of industrial structure,sector structure,and term structure of credit.Various machine learning methods were used,including Logistic Regression,Random Forest,Decision Tree,Support Vector Machine(SVM),Bagging,and Boosting models.The empirical findings indicate that credit expansion plays a crucial role in triggering banking crises.However,total credit is better suited for the early warning of short-term banking crises,whereas credit structure is more useful for the early warning of long-term banking crises.Moreover,in an early warning system,identifying key early warning indicators is more meaningful than merely increasing the number of indicators.Machine learning can somewhat enhance the early warning power,but it may not always be robust.Therefore,more attention should be paid to potential systemic banking crises resulting from an imbalance in credit structure while regulating the total credit threshold.

Fault-tolerant Control of Nonlinear System Using Credit Assign Fuzzy CMAC

The adaptive fault-tolerant control scheme of dynamic nonlinear system based on the credit assigned fuzzy CMAC neural network is presented. The proposed learning approach uses the learned times of addressed hypercubes as the credibility, the amounts of correcting errors are proportional to the inversion of the learned times of addressed hypercubes. With this idea, the learning speed can indeed be improved. Based on the improved CMAC learning approach and using the sliding control technique, the effective control law reconfiguration strategy is presented. The system stability and performance are analyzed under failure scenarios. The numerical simulation demonstrates the effectiveness of the improved CMAC algorithm and the proposed fault-tolerant controller.