Complete Credit Research Articles

Optimalisasi Akurasi Algoritma Naïve Bayes Dengan Metode Syntetic Minority Oversampling Technique (Smote) Pada Data Numerik

This research will classify numerical data, namely loan data taken from Kaggle. The data used amounted to 9578 datasets which included data classes with borrowers able to complete credit as many as 8045 records and loans that could not complete credit as many as 1533 records. From the amount of data there is an imbalance of classes so it is necessary to do balancing in order to get more accurate classification results. The purpose of this research is to improve the accuracy of the Naïve Bayes algorithm in classifying numerical data. Fraud in financial transactions is an example of a case of imbalanced data, where the number of legitimate transactions is much greater than those that are fraudulent. Optimizing accuracy in minority (fraud) classes is very important to avoid losses. The method used to improve the accuracy of the algorithm is the Synthetic Minority Oversampling Technique (SMOTE) by over sampling the minority of the dataset. In addition, it also uses the K-Fold Cross Validation method to evaluate the performance of the algorithm process used. Data preprocessing is done to clean the data from missing and invalid values and normalize the data so that all features are on the same scale and suitable for classification analysis. Based on the results of the analysis conducted, before the application of SMOTE the model's ability to recognize minority classes was 16.1%, while after the application of SMOTE the model's ability to recognize minority classes became 48.8%. besides that, before the application of SMOTE the model was able to predict the minority class correctly in 10 cases while after the application of SMOTE, the model was able to predict the minority class correctly in 102 cases. So it can be concluded that the SMOTE technique is able to improve the ability of the model

The importance of academic advising for dual credit and early college high school students

AbstractAcademic advising for dual credit and early college high school students is significant to postsecondary matriculation and degree completion of these students after high school graduation. Unstructured advising can lead students to take excess credits and have an unclear path to a postsecondary credential. This study focused on the importance of academic advising, ensuring a clear path is identified for each student participating in a dual credit or early college high school program. A qualitative survey identifying high school personnel's understanding of advising, needs to better serve students, and current challenges facing high school counselors is discussed. Quantitative data landscapes student demographics and attempted versus completed credits for both dual credit and early college high school students at multiple high schools. Practitioner recommendations underscore the need for college advising by a college advisor who is familiar with the college advisement process versus a high school counselor who focuses on high school completion needs.

Credit ratings of Chinese online loan platforms based on factor scores and K-means clustering algorithm

The rapid development of Chinese online loan platforms (OLPs), as well as their risks, has attracted widespread attention, increasing the demand for a complete credit rating mechanism. The present study establishes a credit rating indicator system for 130 mainstream Chinese OLPs that combines 12 quantitative metrics of online loan operations similar to commercial bank credit rating indicators, including platform transaction volume and average expected rate of return. We also consider two qualitative indicators of online loan background, namely platform background and guarantee mode, that reflect Chinese characteristics. Subsequently, a factor analysis was conducted to reduce the 14 indicators’ dimensions. The loads of the rating indicators in the resulting rotating component matrix were refined into an OLP operation scale factor, fund dispersion factor, security factor, and profitability factor. Finally, a K-means clustering algorithm was employed to cluster the factor scores of each OLP, thereby obtaining credit rating results. The empirical results indicate that the proposed machine learning–based credit rating method effectively provides early warnings of problem platforms, yielding more accurate credit ratings than those provided by two mainstream online loan rating websites in China, namely, Wangdaitianyan and Wangdaizhijia.

Optimal credit strategy for MSMEs

We address two main issues: one is to quantify the credit risk of enterprises and establish a complete credit risk system, and the other is to give the optimal credit risk strategy for banks. We first analyze and pre-process the data. From the data, we extracted a series of indicators such as the total amount of input and output, and the length of operation. We analyze the credit risk in three directions: strength, stability of supply and demand, and creditworthiness, and establish a credit risk quantification system for the enterprise. Then we quantify the credit risk of the enterprise by entropy method and TOPSIS. Second, a function is fitted to the bank’s customer churn rate and the bank’s lending rate. Using a planning-type model, the credit decisions of the firms are required to be given. We follow the principle of maximizing benefits and minimizing risks to build a multi-objective planning model. We base on the scores of each firm that have been solved, for the classification of firms, and follow the principle of low credit risk, low lending rate, and set the corresponding lending rate for each type of firm. The model is solved by the through-order solution method, and the linear weighting method is used to test the comparison. The credit decision for each enterprise is given.

Does the impact of private education on growth differ at different levels of credit market development?

AbstractUsing an overlapping generations model, we show that the impact of private financing of education on growth depends on credit market development, being positive when credit markets are adequately developed but negative if sufficiently low levels of credit market development occur alongside relatively high private financing intensities. Employing cross‐country data, we find that reduced‐form growth relationships are statistically significant and robust under various controls and samples. We also lay out conditions under which economies with missing credit markets are dynamically efficient and outperform, in terms of growth, economies with complete credit markets. The latter may explain large cross‐country differences in savings and growth, while facilitating the evaluation of policies on financing education.

Construction of Credit Risk Assessment Index System of SMEs based on Blockchain-driven Supply Chain Finance

blockchain-driven supply chain finance (BC-driven SCF) can effectively integrate the credit resources of upstream and downstream enterprises and enhance the financing capacity of all enterprises in the chain, taking the core enterprise as the center and the blockchain technology as the platform, which plays an important role in the upgrading of the industrial chain. In order to accurately and objectively evaluate the SMEs’ credit risk based on BC-driven SCF, we need to establish a scientific and complete credit risk assessment index system, formulate strict design ideas and select appropriate design method. By analyzing the formation mechanism of blockchain-driven supply chain finance model, and using relevant theoretical and empirical methods, this paper puts forward a credit risk assessment index system suitable for the supply chain financial environment. Finally, 27 three-level indicators, 13 two-level indicators and 5 primary indicators were established in index system. This conclusion provides a reference for the application of blockchain in supply chain to reduce the credit risk. At the same time, the selected indicators are more comprehensive, which provides a strong basis for the subsequent construction of credit risk assessment model

Construction and Optimization of Transboundary Business Financial Credit Network in the Era of 5G Communication

The e-commerce industry has broken through the original geographical restrictions, successfully integrated with world trade, and evolved new e-commerce model. The e-commerce model, that is, e-commerce transactions across international trade, has been rapidly expanded. However, e-commerce transactions across international trade have innate virtuality and information imbalance. These problems have caused many credibility problems and, at the same time, restrict and hinder the healthy and sustainable evolution of transactions across international trade. Credit evaluation of transaction entities through credit evaluation models is an effective way to restrict the behavior of transaction entities. However, the existing credit evaluation models lack pertinence and effectiveness when applied in the context of cross-border e-commerce. In the era of 5G communication, building a complete credit evaluation system through 5G-related technologies will certainly become a new way for the stable evolution of transactions across international trade. This not only can effectively control the risks of cross-border trade and improve efficiency but also properly resolve the uncertainty caused by information imbalance. In order to better promote the development of e-commerce, this article establishes an e-business evaluation module based on integrated fusion performance rating. The weight and the membership of each factor are determined by AHP. Finally, the model was verified by an example. The results show that the evaluation system and the method proposed in this paper are feasible and effective for solving practical problems and provide a solid foundation for the construction of the network of my country’s e-commerce credit rating system. Establishing a scientific and reasonable e-commerce integrity evaluation system has very good practical significance.

Study on Risk Analysis and Decision-Making of Small- and Medium-Sized Enterprises on BP Neural Network Algorithm

Artificial neural network algorithm has strong nonlinear mapping function. BP neural network algorithm is the representative of artificial neural network algorithm. Due to the relatively small scale of small, medium, and micro enterprises and lack of mortgage assets, the biggest market risk they usually face is credit risk. In order to avoid huge economic losses for banks, the impact of risk analysis on credit decision-making is the key. Therefore, the establishment of a scientific and complete credit risk assessment system has become the primary task. This paper mainly uses BP neural network algorithm to analyze the risk of small and medium-sized enterprises and solve the problem of credit decision-making. First, analyze the various influencing factors that cause credit risk, and layer the factors according to the degree of risk; secondly, use the fuzzy comprehensive evaluation method in mathematical modeling to rank each factor; then, use the fuzzy analytic hierarchy process to calculate the weight of each indicator factor as calculated by establishing the risk value function, using the function expression of the annual interest rate and the total credit formula to calculate the total credit, which solves the problem of the loan line of each enterprise, and use the optimal linear programming to give priority to the selection of the line and the interest rate. Enterprises: finally, for enterprises that do not have a credit rating, reasonable adjustments are made to the quota by establishing lending qualification expression, taking into account the impact of unexpected external factors, establishing an improved annual interest rate function, and achieving an optimized credit strategy through the establishment and solution of the model. The goal of optimizing credit strategy has been achieved.

Research and Practice of Credits Mutual Recognition for Courses Taken Across Schools in the Context of the Complete Credit System

The emergence of complete credit system, cross-school courses, mutual recognition of credits, mutual selection of courses, mutual employment of teachers and other new types of education methods has promoted the reform and development of Chinese education system, and also reorganized the classroom time structure of Chinese college students, reformed the traditional teaching model and promoted the establishment of the Golden Course. In the way of flipping the classroom, students can make full use of the basic theoretical knowledge of online curriculum resources. Teachers actively guide students to achieve student-led classrooms. At the same time, it strengthens the development and management of online courses, and promotes the sharing of high-quality educational resources and the fairness of education. Meanwhile, it comprehensively improves the quality of education and teaching, reforms traditional education and teaching models. Through reforming the traditional learning process, the new model focuses on research and discussion, strengthens interactive links, and clarifies evaluation standards to profoundly achieves teaching objectives. Under the model of mutual credit recognition, every student in the class is able to participate actively. Students get chances to attend prestige teachers’ classes and experience the unique teaching styles through the online platform, thus maximizing the sharing of teaching resources so that every student has received fair education. In the context of the complete credit system, students are fully respected according to their personality and individuality to gain their individualized education, allowing more flexibility and selectivity in their courses, creating an environment where they can be taught according to their abilities and individuals

Studies on the Implementation Pathway of ‘Credit System’ Reform Through Sino-foreign Joint Education Programs

Sino-foreign Joint Education Programs (SJEPs) is an important strategy for Chinese universities to improve the level of international education. It has been carried out in China for more than 30 years. Yunnan province currently has 23 SJEPs for undergraduates and graduates level. The implementation of SJEPs has promoted the reform of ‘credit system’ in Yunnan universities in recent years. Based on an investigation of current SJEPS and the situation of complete credit system reform in Yunnan province, this paper puts forward an implementation pathway to promote the ‘credit system’ reform through SJEPs.

Construction and Application of the Online Finance Credit Risk Rating Model Based on the Artificial Neural Network

The low-cost, highly efficient online finance credit provides underfunded individuals and small and medium enterprises (SMEs) with an indispensable credit channel. Most of the previous studies focus on the client crediting and screening of online finance. Few have studied the risk rating under a complete credit risk management system. This paper introduces the improved neural network technology to the credit risk rating of online finance. Firstly, the study period was divided into the early phase and late phase after the launch of an online finance credit product. In the early phase, there are few manually labeled samples and many unlabeled samples. Therefore, a cold start method was designed for the credit risk rating of online finance, and the similarity and abnormality of credit default were calculated. In the late phase, there are few unlabeled samples. Hence, the backpropagation neural network (BPNN) was improved for online finance credit risk rating. Our strategy was proved valid through experiments.

Research on the Bank Credit Decision Model Based on Convolutional Neural Network

With the continuous development of small and medium-sized enterprises, the business volume of bank loans is also increasing. Banks need to assess the credit risk of small, medium and micro-sized enterprises based on their strength and reputation, and then determine whether to lend or not and loan strategies such as loan amount, interest rate and term based on factors such as credit risk. Therefore, it is of great significance to study how to establish a complete credit risk assessment system for bank loans. In this paper, a credit decision model based on Convolutional Neural Network is established. Firstly, the Convolutional Neural Network (CNN) is trained by the invoice information and credit rating data of 123 enterprises with credit records, and then the trained CNN model is used to rate the credit of 302 enterprises without credit rating, and then the credit decision model is used to make bank credit decisions when the total credit amount is 100 million yuan. After testing, the accuracy of the Convolutional Neural Network model established in this paper can basically reach more than 80%, and to a certain extent, it can replace the operation of manually rating the credit standing of enterprises within banks.

Credit evaluation method of generating companies considering the market behavior in China electricity market

AbstractSince the reformation of the electricity market of China is increasing and the modernization of the national governance system is being promoted, the electricity market management system needs to be gradually improved. Credit evaluation is an essential part of the electricity market management system. Therefore, a complete credit evaluation system plays a fundamental role in ensuring that the electricity market can operate safely, stably, efficiently, and reliably. The traditional credit evaluation methods focus on the financial situation and credit records of the generating companies. With the gradual development of the electricity market transactions, the market behavior needs to be included in the credit evaluation indicator system to standardize the behavior of the generating companies and reduce the risks of the market transactions. This study establishes a complete credit evaluation system from three perspectives: market behavior, financial situation, and social responsibility. In addition, to determine the weight of indicators in the credit evaluation, the analytic hierarchy process (AHP) algorithm, fuzzy theory, and the non‐dominated sorted genetic algorithm II (NSGA‐II) algorithm are combined. The variance of the evaluation results and the deviation degree of the indicator weight are considered the objective functions of the NSGA‐II algorithm to optimize the indicator weight. Finally, comparing as an example the results of the credit evaluation before and after the optimization of an electricity market shows that the credit evaluation method can effectively distinguish the generating companies that adopt different market behaviors and has a high practical significance.

Research on the Innovation Path of Teaching Management in Undergraduate Colleges and Universities under the Background of Complete Credit System Reform

Based on the background of the reform of complete credit system, this paper analyzes the problems existing in the process of teaching management in traditional undergraduate colleges, systematically studies the backward teaching management and teaching management evaluation system, and puts forward the corresponding countermeasures for the innovation and optimization of teaching management, so as to provide theoretical basis for further research on the innovation path of teaching management and help to cultivate innovative talents in the future.

Do Marriage Markets Respond to a Natural Disaster? The Impact of Flooding of River Kosi in India

This paper studies the impact of flooding of river Kosi on the timing of marriage in Bihar, one of the poorest regions in the world. Using a difference-in-differences design, we show that Kosi floods reduced men’s age at marriage by almost a year and women’s age at marriage by over three months. Kosi floods also decreased secondary school completion rate of married men and women and labor force participation of married women. We interpret these results within a model of marriage markets, where in the absence of complete credit markets, local cultural practices, such as dowry, help smooth consumption in response to adverse income shocks. The impact of Kosi floods is more pronounced among Hindus (for whom dowry is the traditional marriage payment norm) and among the landless (who are more credit-constrained). Against the background of increased frequency of extreme floods in South Asia, this paper highlights a channel through which low mortality natural disasters can have long-lasting impacts.

“Internet + Community”

After the implementation of complete credit system, colleges gradually lead the class students to individualization, which is consistent with the characteristics of individualization in the Internet age. And the trend of “Internet community” constructed by Internet, from the earlier micro-blog to the current WeChat group or Moments, also provides us with new ideas on how to carry out values education for "individualized" college students under the complete credit system. The promotion of complete credit system in colleges and the trend of the "Internet community" enable colleges to effectively carry out values education of college students through a new way. Hence, colleges should, on one hand, pay more attention to the implicit education, understand and master the dissemination rules of the network platform, as well as utilize the new carrier of values education innovatively. On the other hand, colleges are also encouraged to construct the "Internet community" type education mode from the three dimensions of perfecting the community structure, adjusting the group mentality and emphasizing the guidance of public opinion.

Exploring the Effects of Learning Environment on Transfer, Student Perceptions, and Instructor Accommodations in an Undergraduate Course Sequence

ABSTRACTPrevious studies have compared student performance for the same or similar classes delivered both online and face-to-face. However, few studies have explored the role that a change in learning environment plays in the student’s ability to transfer skills and knowledge gained in prerequisite courses to follow-on, or post-requisite, courses. The purpose of this study was to examine the relationship between student demographic factors, learning environments, and the transfer of skills and knowledge in undergraduate accounting education. In addition, this study examined student perceptions of online learning both before and after the course learning sequence, and the specific accommodations instructors made to their instructional design to facilitate the delivery medium change in learning environment. Data for this study was collected from 2,324 students and their corresponding instructors participating in a sequence of two undergraduate accounting courses at a large Midwestern U.S. university. Results revealed a positive relationship between a student’s performance in post-requisite coursework and GPA, the number of completed credits, and enrollment status (full or part-time). Instructors made delivery medium specific adjustments in course topic coverage, student participation, and group work to accommodate for the perceived capabilities of the learning environment.

Frailty correlated default on retail consumer loans in Zimbabwe

There has been increasing availability of consumer credit in Zimbabwe, yet the credit information sharing systems are not as advanced. Using frailty survival models on credit bureau data from Zimbabwe; this study investigates the possible underestimation of credit losses under the assumption of independence of default event times. The study found that adding a frailty term significantly improved the models, thus indicating the presence of unobserved heterogeneity. The major policy recommendation is for the regulator to institute appropriate policy frameworks to allow robust and complete credit information sharing and reporting as doing so will significantly improve the functioning of the credit market.

Frailty correlated default on retail consumer loans in Zimbabwe

There has been increasing availability of consumer credit in Zimbabwe, yet the credit information sharing systems are not as advanced. Using frailty survival models on credit bureau data from Zimbabwe; this study investigates the possible underestimation of credit losses under the assumption of independence of default event times. The study found that adding a frailty term significantly improved the models, thus indicating the presence of unobserved heterogeneity. The major policy recommendation is for the regulator to institute appropriate policy frameworks to allow robust and complete credit information sharing and reporting as doing so will significantly improve the functioning of the credit market.

The Labor Market Returns to a Community College Education for Noncompleting Students

ABSTRACTIn this study, I used data from California to estimate the returns to a community college education that does not result in a postsecondary credential. I found strong, positive returns to completed credits in career and technical education (CTE) fields that are closely linked to employment sectors that are not credential-intensive (sectors in which employment often does not require a college degree), such as public safety, skilled blue-collar trade and technical work, and accounting and bookkeeping, among others. In these sectors, students were able to convert the human capital acquired in their coursework into returns that far exceeded the cost of the coursework itself, making some noncompleting educational pathways a rational means of securing earnings gains. This finding is consistent with emerging research on skills-builder students and other segments of the community college student population who exhibit coherent patterns of course taking and enrollment that typically do not result in a postsecondary credential. Further investigations of high-return noncompleting pathways are warranted and could help colleges to target efforts to grow postsecondary completion opportunities for students through short-term certificates programs, while also aiding efforts to communicate to stakeholders the successes that cannot be measured by counting credentials or transfers.