Credit Evaluation System Research Articles

Social credit scoring system and corporate pollution governance: Insights from China's Social Credit System Construction

This study examines the effect of China's Social Credit System (CSCS) construction as a quasi-natural experiment on corporate emissions. The System collects and shares credit information, including environmental credit for firms. Using 30,766 firm-year observations of Chinese non-financial listed firms from 2007 to 2021 and pilot programs in the construction of the CSCS in various cities, we document that firms located in cities with the CSCS system lower their emissions than those in cities without the CSCS system. Additional analysis suggests that the lower emission for the CSCS firms is due to enhancing environmental information disclosure, advancing technological progress, and alleviating financial constraints. The moderating analysis reveals that the impact of the CSCS on restraining corporate emissions is more salient when a firm: (1) belongs to a heavily polluting industry or is privately-owned; (2) has management myopia or lack of environmental awareness; or (3) has a weak internal and external monitoring.

A SYSTEMATIC LITERATURE REVIEW OF PREDICTIVE MODELS AND ANALYTICS IN AI-DRIVEN CREDIT SCORING

This systematic review examines the transformative role of AI-driven models in credit scoring, highlighting their advances over traditional statistical methods in terms of predictive accuracy, adaptability, and inclusivity. By synthesizing findings from 70 studies, this review demonstrates that machine learning techniques, particularly ensemble models such as random forests and gradient boosting, effectively capture complex, non-linear relationships in borrower data, providing more accurate risk assessments across diverse demographics. Deep learning models, especially convolutional and recurrent neural networks, extend credit scoring capabilities to unstructured and alternative data sources, supporting financial inclusion by enabling assessments of individuals without traditional credit histories. Hybrid models that integrate logistic regression with neural networks offer an optimal balance between interpretability and predictive power, addressing regulatory demands for transparency while maintaining robust accuracy. Ensemble techniques like stacking and blending enhance model adaptability, allowing credit scoring systems to integrate multiple perspectives and improve prediction accuracy in varied borrower contexts. Despite these advancements, challenges remain in the form of ethical concerns and the need for model interpretability, particularly with complex deep-learning architectures. The review underscores the importance of developing fairness-aware and explainable AI frameworks to ensure that as AI-driven credit scoring evolves, it remains both transparent and equitable. These insights suggest that with careful attention to ethics and transparency, AI has the potential to create a more inclusive and resilient credit scoring landscape, accommodating the needs of an increasingly diverse global population.

Juxtaposing Gender Differentials in Credit Assessment of Farmers in Nigeria: A Hybridized Credit-Scoring Approach

Using data from 360 smallholder farmers in Southeast Nigeria, the study creates the architecture for a new farmer's hybrid credit rating system used in classifying farmers who applied for microfinance loans based on their creditworthiness. We discovered new evidence that the hybridized credit scoring algorithm demonstrated unprecedented concordance in assessing the financial viability of farmers along gender lines. The discriminant analysis, in particular, closely aligned with the credit score model, with 34.4% and 46.7% of male and female farmers grouped as creditworthy, reflecting the model's estimates of 33.3% and 45.5%, indicating gaps of 12.3% and 12.2%, respectively, to the advantage of the female farmers. Our findings further suggest that annual income, marital status, and farm size strongly influence the separation between creditworthy and non-creditworthy farmers. While age, loan term, and a history of defaults had a negative impact on discrimination, in light of the findings, we recommend a collaboration between authorities, financial institutions, and extension workers in offering tailored trainings to both male and female farmers, assisting them in meeting up-to-date credit prerequisites, adopting modified farming techniques, and improving their general preparedness to be accepted for loans in this changing credit evaluation landscape so as to bridge the disparity and promote financial inclusion for farmers irrespective of gender affiliations.

Clinical Reasoning Skills Among Second-Phase Medical Students in West Bengal, India: An Exploratory Study.

Introduction Proper application of clinical reasoning skills is essential to reduce diagnostic and management errors. Explicit inclusion of training and assessment of clinical reasoning skills is the demand of time. The study intended to measure the clinical reasoning skills of second-phase undergraduate students in a medical college inWest Bengal, India, and its distribution across several individual variables. Methods The clinical reasoning skills of 105 undergraduate medical students were assessed in a cross-sectional exploratory study using key feature questions (KFQs) with the partial credit scoring system. Six case vignettes aligned to the core competencies in the subject of pharmacology, pathology, and microbiology were designed and validated by the subject material experts for this purpose. The responses of the participants were collected through Google Forms (Google, Mountain View, CA) after obtaining written informed consent. The scores obtained in all KFQs were added and expressed in percentage of the maximum attainable score. Results The mean (±SD) clinical reasoning score of the participants was 42.5 (±12.6). Only 29.6% of respondents scored ≥ 50. Students with higher subjective economic status (p-value = 0.01) and perceived autonomy (p-value < 0.001) were more likely to have higher clinical reasoning scores. The marks obtained in previous summative examinations were significantly correlated with clinical reasoning scores. Conclusion Average score < 50.0 and inability to score ≥ 50.0 by more than two-thirds of the participants reflected the deficit in the clinical reasoning skills of second-phase MBBSstudents. The association of clinical reasoning skills with economic status, autonomy, and previous academic performances needs further exploration.

Can the construction of the social credit system improve the efficiency of corporate investment?

Based on the quasi-natural experiment of China's pilot reform of social credit system, this paper examines the effect and mechanisms of the construction of social credit system on corporate investment efficiency using the data of A-share listed companies in Shanghai and Shenzhen from 2005 to 2022. The study presents the following findings. First, the construction of social credit system significantly improves corporate investment efficiency, through three main paths: alleviating information asymmetry, improving internal corporate governance, and strengthening market competition pressure. Second, there is significant heterogeneity in the promotion effect of the construction of social credit system on corporate investment efficiency, which is more pronounced for non-state-owned enterprises and enterprises in monopolistic industries. Based on the above research conclusions, this paper puts forward policy suggestions such as establishing a comprehensive credit information sharing platform, promoting the credit rating system, strengthening credit supervision and risk early warning to improve the social credit system.

An enhanced consensus algorithm for blockchain

Consensus plays a crucial role in blockchain technology, with the delegated proof of stake (DPoS) consensus mechanism commonly utilized in both public and hybrid chains. However, the current DPoS mechanism faces challenges such as low node engagement in voting and potential security risks posed by malicious nodes. In response, we propose the DL-DPoS (deep link–delegated proof of stake) mechanism, which builds upon the DPoS framework. The DL-DPoS incorporates the LINK incentive mechanism to encourage inactive nodes to participate in voting and leader selection. Furthermore, a comprehensive credit scoring system based on wealth, performance, and stability is introduced to enhance the security of elected nodes. The verification process is optimized to involve all nodes except the leader node, and mechanisms are in place to handle malicious attacks by degrading or removing offending nodes and redistributing their responsibilities to the LINK group. Performance testing of the DL-DPoS mechanism, conducted through blockchain simulation tests using the GO language, shows a 23% increase in throughput compared to DPoS, with over 95% node participation and improved distribution of rights and equity. These results indicate the enhanced performance, security, and stability of the DL-DPoS consensus mechanism.

Design and Implementation of an Intelligent Campus Second-hand Trading Platform based on Microservices and Cloud Computing

With the rapid development of smart campuses, traditional campus second-hand trading methods can no longer meet the growing needs of students. This paper proposes a design scheme for an intelligent campus second-hand trading platform based on microservices and cloud computing, which provides customized trading experience for users by introducing advanced recommendation algorithms. The microservice architecture gives the platform extremely high module decoupling and autonomous scalability, ensuring the high availability and agile response of the system. The recommendation system uses a hybrid algorithm, combining content filtering and collaborative filtering, and continuously optimizes the recommendation results through machine learning, so as to accurately capture user preferences. One of the features of the platform is its community-driven data feedback mechanism, which not only can adjust the recommendation strategy in real-time but also can promote the sustainable development of campus economy and culture through data mining. In addition, the research explores a novel credit evaluation system design to establish a trustworthy trading environment. The experimental results show the technical innovation and high efficiency of the platform in multiple dimensions, which is significantly superior to the existing solutions. The successful implementation of this research indicates that the campus trading platform is moving towards a more intelligent and personalized direction, and is expected to play a key role in the smart campus ecosystem.

Exploring Market Credit Evaluation System of Tourism Enterprises from Tourist Perspective

ABSTRACT This study employs literature review and expert consultation methods to construct dimensions and items for evaluating the market credit of tourism enterprises from tourist perspective. A Hierarchical Analysis Method was used to develop the market credit evaluation system for tourism enterprises. The research findings indicate that the evaluation system comprises 7 dimensions and 37 items, which include: credit capacity, credit regulation, credit foundation, credit behavior, economic credit, employee credit, and integrity culture. The Hierarchical Analysis Method was applied to determine the weight for these dimensions and items. The credit foundation of tourism enterprises was found to have the highest weight, followed by credit regulation, credit capacity, credit behavior, economic credit, employee credit, and integrity culture. Theoretically, this research enriches the theory of tourism enterprise credit and provides a measurement tool for evaluating market credit. Practically, it offers targeted suggestions and strategies for tourism enterprises to enhance their market credit.

A Quantitative Analysis of Default Risk Using Machine Learning and SHAP Value Interpretation

Abstract In finance, creating a model that balances risk reduction with opportunity is essential. This investigation addresses the necessity for risk evaluation frameworks that combine efficiency with adaptability, thus preserving opportunities for transactions critical to some organizations. The present study identifies, within a factoring process involving two key players: the invoice seller and the debtor, the essential variables that determine the likelihood of the debtor defaulting on the invoice payment. The event of non-payment is most often associated with the debtor's inability to pay due to insolvency, making it crucial in this type of activity to emphasize an efficient credit scoring system capable of proactively highlighting a debtor company with a high risk of default. Nonetheless, some companies pass this filter and enter the factoring process, but end up being unable to pay. The study identifies them based on a set of real data and uses supervised machine learning techniques to select the optimal classification model, also highlighting the variables with a major impact on the target. The specialized literature is focused on identifying the models that perform best in the credit scoring activity or studies that identify the non-payment behavior of clients. What this work adds is the combination of these two dimensions, for example, it provides an additional filter to credit scoring, using parameters identified as essential in determining defaulters and using them as inputs for an unsupervised learning model, thus classifying the entire population of companies in Romania to identify clusters containing the highest proportion of non-payment companies.

An Exploratory Study on the Application of Blockchain Technology to the Chinese Ship Auction Market

The Chinese ship trading market has undergone remarkable development, transitioning into a global exemplar of ship transactions. Nevertheless, this market continues to confront a series of challenges, encompassing low transaction rates, delayed transaction values, and market instability. Blockchain, with its secure, transparent, and tamper-resistant technological features, presents a potential solution for the ship trading market. Through its decentralized architecture, blockchain technology can reduce platform operational costs, enhancing market competitiveness. Concurrently, the utilization of asymmetric encryption technology can enhance the security of platform data, effectively addressing privacy concerns. Furthermore, through a sharing mechanism, blockchain can aid in establishing a unified credit evaluation system, thereby increasing market trust to address the absence of a credit evaluation system. Moreover, blockchain technology can be employed to construct a risk identification mechanism, fortifying the platform's regulatory model to address regulatory deficiencies.

Credit Scoring System Using Machine Learning

This research paper offers a comprehensive examination of credit scoring systems. We will explore the current methodologies employed, the challenges encountered, and potential avenues for improvement. Credit scoring plays a critical role in financial decision-making, impacting both individual access to credit and lender risk management strategies. The paper will analyze traditional credit scoring models alongside emerging trends designed to enhance the accuracy and fairness of credit assessments. Additionally, we will discuss the ethical considerations surrounding credit scoring and propose recommendations for future advancements.

Credit Issues and Regulatory Strategies in Platform Economy

Platform economy is an important part of digital economy. With rapid development in recent years, platform economy has a series problem which has definitely hindered the normal operation of platform economy because of lax supervision of regulators. It's necessary to focus on building regulatory strategies: platform enterprise credit file, platform enterprise credit evaluation system, platform enterprise access mechanism and platform enterprise exit mechanism to realize the healthy and sustainable development of platform economy.

The Machine Learning Methods For Micro-Credit Scoring: The Case Of Micro-Financing In Mongolia

As a result of growing digital technologies in the financial sector, the traditional slow lending process is being replaced by fast and easy digital lending systems that can make decisions in real time. Both lenders and borrowers have experienced the benefits of digital lending, the activities of microfinance institutions have expanded rapidly and the volume of digital microloans has increased significantly worldwide, including Mongolia. At the same time with the growing volume of digital microloans in Mongolia, the rationality of credit risk management has been becoming more critical. Credit quality is the most important factor in optimal credit risk management. It depends on determining the customer's creditworthiness and making accurate credit decisions. This research focuses on a credit scoring system to improve the digital loan evaluation system of the Mongolian microfinance institute. This study aims to contribute to the development of possible credit scoring systems for Mongolian microfinance institutions by comparing several machine-learning approaches based on loan datasets of a non-banking microfinance institute in Mongolia. The result shows the ensemble methods Random Forest and XGBoost Tree's accuracies are higher than other machine learning models for the microloan borrowers' repayment status prediction.

Predicting Bank Loan Eligibility Using Machine Learning

With the increasing needs of people, the demand for bank loans also rises. Each day, banks receive numerous loan applications from various customers and individuals, however, not all applicants are approved. Banks typically assess an applicant's eligibility before processing a loan application, a process that can be time-consuming and complex. In evaluating loan applications and making credit decisions, banks often rely on their credit scoring and risk assessment systems. Nevertheless, there are still cases where some applicants default on their payments annually, resulting in significant financial losses for financial institutions. In this research, we utilized machine learning algorithms to analyze a dataset of approved loans and predict which applicants are most deserving of a loan. The study incorporates customers' historical data, including factors such as age, income type, loan annuity, last credit bureau report, organization type, and length of employment. Various machine learning methods like Random Forest, XGBoost, Adaboost, Lightgbm, Decision tree, and K-Nearest Neighbor were utilized to identify the most influential features that impact prediction outcomes. These algorithms were then compared using standard metrics, with Logistic Regression achieving the highest accuracy rate of 92%. Key Words: Bank Loans, Logistic Regression, Credit Report, Risk Assessment

Decoding CIBIL: Understanding the Structure and Strategies for Improvement

This research paper delves into the intricate dynamics of credit scoring systems, particularly focusing on understanding the structure of CIBIL scores and delineating strategies for improving creditworthiness. Drawing upon theoretical frameworks from financial economics and regulatory economics, the study elucidates key factors influencing credit scores, including payment history, credit utilization, credit type, and duration. Through a mixed-methods approach encompassing secondary data analysis from the official websites of TransUnion CIBIL India and Bajaj Finance Limited, as well as qualitative exploration through thematic analysis, the research uncovers actionable insights for individuals seeking to enhance their credit profiles.By bridging theoretical insights with empirical findings, this study contributes to the literature on credit management and equips individuals with knowledge essential for navigating the complexities of credit assessment and improving financial resilience

Integrating Algorithmic Decision Making into Small Business Credit Initiatives: a path to Enhanced Efficiency and Inclusive Economic Growth

Purpose: This paper addresses the challenges faced by small businesses in accessing credit through Small Business Credit Initiatives (SBCI) in the United States. Despite the success of SBCI in creating jobs and fostering economic growth, there are limitations in the evaluation process.&#x0D; Methodology: The research design integrates advanced algorithmic decision-making, machine learning, and LLMs into existing credit evaluation process. Primary data is collected from various sources, including financial and business history, market sentiments, external factors, and utilization of sampling techniques if required. Document review, surveys and digital platforms are used for collecting data for LLMs to extract insightful information from complex sources. This comprehensive approach, combining with traditional and innovative methods, aims to establish a robust foundation for developing and evaluating a fair, efficient, and adaptive credit evaluation system for small business credit initiatives.&#x0D; Findings: The proposed framework integrates external market factors and use of LLMs for document review on top of primary data sources currently in adaption. Data processing could be amended by extracting features by using advanced natural language processing to enhance feature space by collecting valuable information which is expected to enhance predictive power, adjustment of thresholds and decision making along with a feedback loop.&#x0D; Unique Contribution to Theory, Policy, and Practice: Unique framework to accelerate small business credit initiatives by developing a new process of selecting and evaluating machine learning model centered on addressing associated risks, adapting to changes in government policy, improving current procedures, and incorporating feedback from stakeholders and applicants. This is done in an organized manner, with a focus on monitoring and maintaining algorithmic decision models.

Optimization Strategy of Credit Scoring System based on Support Vector Machine

This article proposes a novel optimization strategy for credit scoring systems that exploits the capabilities of SVM. Focusing on the importance of personal credit scoring in today's credit dynamics, the article explores SVM's versatility in various domains through a literature review. The theoretical background underscores the unique approach and computational efficiency of SVM. The optimization strategy encompasses four critical aspects: debt solvency, earning potential, operational prowess, and growth capability using metrics such as asset-liability ratios. Experimental validation with credit card datasets from Australia and Germany illustrates the nuanced relationship between different K-values and performance metrics, and demonstrates the adaptability of SVM in improving credit scoring. In short, the article presents an original, comprehensive approach to credit risk management that integrates theoretical foundations, literature findings, and empirical experiments to improve the accuracy of credit scoring in the dynamic economic landscaper.

Taking P2P Online Lending as An Example

This paper is based on the critical reflection of P2P online lending, with the supervision mechanism as the main analysis Angle, credit assessment and risk prevention and control as the auxiliary to carry out the discussion of P2P online lending, hoping to provide more thinking for the future development of China's Internet + finance. The main research method adopted in this paper is case study, mainly based on typical cases at home and abroad to carry out analysis. After case analysis, the conclusions are as follows: 1. In terms of supervision mechanism: (1) Let the platform and the bank reach a commercial cooperation relationship. (2) When the debt relationship is formed, it is suggested to sign smart contracts. (3) When the debtor registers as a user on the platform, it can use the bank to evaluate the assets and liabilities. (4) The platform can also learn from the Dutch supervision mechanism for creditors on the Internet platform. 2. In terms of credit evaluation, China can build a credit evaluation system belonging to Internet finance in the credit investigation system of the People's Bank of China. 3. In terms of risk prevention and control, default records should be synchronized with banks and default costs should be raised.

Blockchain-based multi-institution collaborative credit evaluation modeling method

Multi-institution credit data sharing-aggregation can improve the accuracy of credit evaluation, however, it also encounters the problems of data fraud, and the privacy, compliance of collaborative modeling. Faced with increasingly stringent and comprehensive privacy protection regulations, the potential security risk call into question the current data sharing-aggregation mode. Blockchain has shown that trusted and auditable computing is possible using a decentralized network of peers, and the immutable distributed ledger. Moreover, oblivious transfer (OT) guarantees the confidentiality of cross-institution transmission and calculation. The integration of blockchain with OT is rapidly increasing in computing environment. In this paper, we design and implement a blockchain-OT-based credit evaluation system dubbed as MEChain. Blockchain acts as a distributed ledger which facilitates efficient and credible data sharing-aggregation. Combined the 1-out-of-N OT, data transformation is used for raw data encryption achieving private, secure, and compliant sharing-aggregation operations. The calculation case and security analysis, prototype system implementation, performance and accuracy rate analysis are presented to validate the proposed solution.

Blockchain-driven framework for financing credit in small and medium-sized real estate enterprises

PurposeThis study aimed to improve the financing credit evaluation for small and medium-sized real estate enterprises (SMREEs). A financing credit evaluation model was proposed, and a blockchain-driven financing credit evaluation framework was designed to improve the transparency, credibility and applicability of the financing credit evaluation process.Design/methodology/approachThe design science research methodology was adopted to identify the main steps in constructing the financing credit model and blockchain-driven framework. The fuzzy analytic hierarchy process (FAHP)–entropy weighting method (EWM)–set pair analysis (SPA) method was used to design a financing credit evaluation model. Moreover, the proposed framework was validated using data acquired from actual cases.FindingsThe results indicate that: (1) the proposed blockchain-driven financing credit evaluation framework can effectively realize a transparent evaluation process compared to the traditional financing credit evaluation system. (2) The proposed model has high effectiveness and can achieve efficient credit ranking, reflect SMREEs' credit status and help improve credit rating.Originality/valueThis study proposes a financing credit evaluation model of SMREEs based on the FAHP–EWM–SPA method. All credit rating data and evaluation process data are immediately stored in the proposed blockchain framework, and the immutable and traceable nature of blockchain enhances trust between nodes, improving the reliability of the financing credit evaluation process and results. In addition, this study partially fulfills the lack of investigations on blockchain adoption for SMREEs' financing credit.