Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

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.

The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight

The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi-time points and multi-indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.

The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight

The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the Electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi time points and multi indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the Multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.

MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario.First,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise information.The proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model updating.Secondly,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and interpretability.In addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model.The results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify defaulters.Finally,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

Prioritizing real estate enterprises based on credit risk assessment:an integrated multi‑criteria group decision support framework

Credit risk assessment involves conducting a fair review and evaluation of an assessed subject’s solvency and creditworthiness.In the context of real estate enterprises,credit risk assessment provides a basis for banks and other financial institutions to choose suitable investment objects.Additionally,it encourages real estate enterprises to abide by market norms and provide reliable information for the standardized management of the real estate industry.However,Chinese real estate companies are hesitant to disclose their actual operating data due to privacy concerns,making subjective evalu-ation approaches inevitable,occupying important roles in accomplishing Chinese real estate enterprise credit risk assessment tasks.To improve the normative and reliability of credit risk assessment for Chinese real estate enterprises,this study proposes an integrated multi-criteria group decision-making approach.First,a credit risk assessment index for Chinese real estate enterprises is established.Then,the proposed framework combines proportional hesitant fuzzy linguistic term sets and preference ranking organization method for enrichment evaluation II methods.This approach is suitable for processing large amounts of data with high uncertainty,which is often the case in credit risk assessment tasks of Chinese real estate enterprises involving massive subjec-tive evaluation information.Finally,the proposed model is validated through a case study accompanied by sensitivity and comparative analyses to verify its rationality and feasibility.This study contributes to the research on credit assessment for Chinese real estate enterprises and provides a revised paradigm for real estate enterprise credit risk assessment.

Hesitant Fuzzy-Sets Based Decision-Making Model for Security Risk Assessment

Security is an important component in the process of developing healthcare web applications.We need to ensure security maintenance;therefore the analysis of healthcare web application’s security risk is of utmost importance.Properties must be considered to minimise the security risk.Additionally,security risk management activities are revised,prepared,implemented,tracked,and regularly set up efficiently to design the security of healthcare web applications.Managing the security risk of a healthcare web application must be considered as the key component.Security is,in specific,seen as an add-on during the development process of healthcare web applications,but not as the key problem.Researchers must ensure that security is taken into account right from the earlier developmental stages of the healthcare web application.In this row,the authors of this study have used the hesitant fuzzy-based AHP-TOPSIS technique to estimate the risks of various healthcare web applications for improving security-durability.This approach would help to design and incorporate security features in healthcare web applications that would be able to battle threats on their own,and not depend solely on the external security of healthcare web applications.Furthermore,in terms of healthcare web application’s security-durability,the security risk variable is measured,and vice versa.Hence,the findings of our study will also be useful in improving the durability of several web applications in healthcare.

Understanding Credit Risk in Internet Consumer Finance:An Empirical Analysis with a Focus on the Young Generation

In recent years,internet finance has garnered increasing attention from the public.Online lending,emerging within the framework of Internet finance as a pivotal component,has witnessed substantial growth.While online credit,within the realm of Internet finance,presents numerous advantages over traditional lending,it concurrently exposes a plethora of credit risk issues.This study aims to facilitate the effective utilization of online credit tools by the young generation within the context of Internet finance.Additionally,it seeks to ensure the overall stability of the Internet finance environment and mitigate risks for the youth.Given the significance of understanding credit risk management for college students in the age of internet finance,this paper adopts the logistic model to evaluate credit risk in internet consumer finance and provides pertinent recommendations from the perspective of the young generation.

Method for Risky Multiobjective Group Decision-Making and Its Application

The multiobjective group decision-making problem under risk is common in reality. This paper focuses on the study about risky multiobjective group decision-making problem where the index value is not certain. We give indexes classifying method and index normalizing formula of this type problem. By building objective function that minimizes general weighted distance from every alternative to the relatively best and worst alternative, the optimal membership degree of every decision-maker to every alternative can be obtained, and by building another objective function that minimizes general weighted distance from the optimal membership degree of every decision-maker to every alternative to the group optimal alternative and the group inferior alternative, the optimal membership degree of every decision-maker to every alternative can be obtained, which are both based on probability theory and fuzzy theory. Aftermost a model is established which collects group preferences. This method provides a new idea and approach for solving multiobjective decision-making problem among uncertain system, which is applicable for practical problem. Finally a case study shows a satisfactory result.

GIS Application in Urban Flood Risk Analysis: Midar as a Case Study

The significance of this study lies in its exploration of the advanced applications of Geographic Information Systems (GIS) in assessing urban flood risks, with a specific focus on Midar, Morocco. This research is pivotal as it showcases that GIS technology is not just a tool for mapping, but a critical component in urban planning and emergency management strategies. By meticulously identifying and mapping flood-prone areas in Midar, the study provides invaluable insights into the potential vulnerabilities of urban landscapes to flooding. Moreover, this research demonstrates the practical utility of GIS in mitigating material losses, a significant concern in flood-prone urban areas. The proactive approach proposed in this study, centered around the use of GIS, aims to safeguard Midar’s population and infrastructure from the devastating impacts of floods. This approach serves as a model for other urban areas facing similar challenges, highlighting the indispensable role of GIS in disaster preparedness and response. Overall, the study underscores the transformative potential of GIS in enhancing urban resilience, making it a crucial tool in the fight against natural disasters like floods.

Optimal Decision-Making of Trans-Provincial Electricity Market Subjects with Risks under Renewable Portfolio Standards

The randomness and uncertainty of renewable energy generation are expected to significantly change the optimal decision-making of trans-provincial electricity market subjects.Therefore,it is beneficial to optimize the interests of each of these subjects,considering the unpredictable risks of renewable energy under the renewable portfolio standards(RPS)and researching their effects on the optimal decision-making of transprovincial electricity market multi-subjects.First,we develop a trans-provincial trading market mechanism for renewable energy and clarify the electricity supply and demand relation and the green certificates supply and demand relation of trans-provincial electricitymarketmulti-subjects.Then,under the RPS,we construct a multi-subject game model of the power supply chain that recognizes the risks,and adopt the reverse induction method to discuss the optimum risk-taking judgment of each subject in the trans-provincial electricity market.Finally,we useMATLAB to verify the viability and efficacy of the proposed gamemodel,and obtain a certain reference value for the optimal decision-making of trans-provincial electricity market subjects.In summary,we consider the uncertainty risks of renewable energy under RPS,study the effects of the green certificate price and risk aversion coefficient in the RPS mechanism on the optimal decisionmaking of trans-provincial electricity market subjects,and obtain the changing trends of two different power products and those of different electricity market subjects under the influence of the green certificate price and risk aversion coefficient,which have a certain reference value for studying the factors affecting the optimal decision-making of trans-provincial electricity market subjects.

Consideration of the Application and Risk Prevention of VAT Tax Rebate Policy for Construction Enterprises at the End of the Period

The“Announcement on Deepening the Value-Added Tax Reform”clearly outlines the preferential policy regarding incremental retention tax rebates.With the advancement of value-added tax(VAT)reform and the improvement of VAT legislation in China,VAT tax planning for construction enterprises,particularly related to retained tax credits,has become routine.This paper,focusing on the characteristics of construction enterprises,analyzes VAT retained tax credits at the end of the period,the status of tax refunds,practical issues,and related processes,and offers suggestions for policy application and risk prevention.

基于Credit Risk+模型的互联网金融信用风险估计

文章基于Credit Risk+模型,使用互联网信贷平台四个行业的贷款数据,在不同置信水平下,对互联网金融的信用风险水平进行比较分析。结果表明,行业风险因子间协方差相等时,复合伽玛Credit Risk+模型和多元系统风险Credit Risk+模型计算结果几乎一致,与CSFB Credit Risk+模型和两阶段Credit Risk+模型相比能更好地反映贷款组合的非预期损失。行业风险因子间协方差不等时,多元系统风险Credit Risk+模型能克服其他Credit Risk+模型的缺陷,综合考量系统风险和行业风险的影响,能更好地估计贷款组合的信用风险水平。

基于损失程度变化的CreditRisk^+的鞍点逼近

传统的CreditRisk+模型在度量信用风险过程,假定违约损失是给定不变的,而近年的实证研究表明在实际的金融市场中违约损失是变化的。针对传统模型这一假设的不合理性,本文对模型作了发展。新模型充分考虑了违约时损失程度的变化,用β分布来刻画这种变化,并利用鞍点逼近给出了信用风险的度量,改进了传统递推算法的不足。最后进行数值模拟以说明方法的有效性。

农地经营权抵押贷款信用风险影响因素及其衡量研究——基于CreditRisk+模型的估计

基于1 173个贷款样本数据,运用Logistic回归分析农地经营权抵押贷款信用风险影响因素并预测违约概率,依据CreditRisk+模型,对农地经营权抵押贷款信用风险衡量进行研究,并进行了压力测试。研究表明,农地经营权抵押贷款信用风险主要受到抵押土地因素、保险与政策因素的影响;影响因素的风险程度具有次序性;贷款期限和农业生产周期不匹配是农地经营权抵押贷款面临的突出矛盾;土地经营权来源不同的贷款风险程度存在明显差异;农地经营权抵押贷款预期损失和非预期损失占VaR比例结构合理,极端情景出现时预期损失会有明显波动。提出应瞄准贷款对象、精确贷款条款和强化风险处置,促进农地经营权抵押贷款顺利开展。

基于CreditRisk+模型的零售贷款经济资本计量方法

针对零售贷款的信用风险特征,在CreditRisk+框架下,采用非线性时变比例模型分类测算零售贷款违约概率,并引入FFT-Panjer算法简化零售贷款组合的损失分布计算,提出了零售贷款信用风险的经济资本计量方法。同时通过算例分析论证了该方法在我国商业银行运用的科学性和可行性。

CreditRisk^+模型在商业银行信贷风险管理中的应用

使用CreditRisk^+(信贷风险附加)模型对选自某商业银行的贷款的资产组合进行风险测度。得到了资产组合中各债务人的预期违约损失和风险贡献,资产组合的预期违约损失分布,以及各置信度损失水平下的临界值等信息,从而完成了资产组合风险的测量。根据测量结果,对资产组合进行了预期违约损失、风险贡献、经济资本和信用准备金等分析,在此基础上提出目前我国商业银行信贷风险管理的现实选择是CreditRisk^+这种违约模式的模型。

A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance

Background:Supply chain finance(SCF)is a series of financial solutions provided by financial institutions to suppliers and customers facing demands on their working capital.As a systematic arrangement,SCF utilizes the authenticity of the trade between(SMEs)and their“counterparties”,which are usually the leading enterprises in their supply chains.Because in these arrangements the leading enterprises are the guarantors for the SMEs,the credit levels of such counterparties are becoming important factors of concern to financial institutions’risk management(i.e.,commercial banks offering SCF services).Thus,these institutions need to assess the credit risks of the SMEs from a view of the supply chain,rather than only assessing an SME’s repayment ability.The aim of this paper is to research credit risk assessment models for SCF.Methods:We establish an index system for credit risk assessment,adopting a view of the supply chain that considers the leading enterprise’s credit status and the relationships developed in the supply chain.Furthermore,We conducted two credit risk assessment models based on support vector machine(SVM)technique and BP neural network respectly.Results:(1)The SCF credit risk assessment index system designed in this paper,which contained supply chain leading enterprise’s credit status and cooperative relationships between SMEs and leading enterprises,can help banks to raise their accuracy on predicting a small and medium enterprise whether default or not.Therefore,more SMEs can obtain loans from banks through SCF.(2)The SCF credit risk assessment model based on SVM is of good generalization ability and robustness,which is more effective than BP neural network assessment model.Hence,Banks can raise the accuracy of credit risk assessment on SMEs by applying the SVM model,which can alleviate credit rationing on SMEs.Conclusions:(1)The SCF credit risk assessment index system can solve the problem of banks incorrectly labeling a creditworthy enterprise as a default enterprise,and thereby improve the credit rating status in the process of SME financing.(2)By analyzing and comparing the empirical results,we find that the SVM assessment model,on evaluating the SME credit risk,is more effective than the BP neural network assessment model.This new assessment model based on SVM can raise the accuracy of classification between good credit and bad credit SMEs.(3)Therefore,the SCF credit risk assessment index system and the assessment model based on SVM,is the optimal combination for commercial banks to use to evaluate SMEs’credit risk.

Empirical Study on Credit Risk of Our Listed Company Based on KMV Model

KMV model is one of the most important credit risk evaluation models in the world. It uses B-S option pricing and Morton formula based on the market value and volatility of the company’s equity, debt maturities, risk-free interest rates and the book value of liabilities to estimate the market value of the company’s assets and the volatility of the asset value. In this paper, based on the theory of KMV model, we can derive the listed company’s default rate, and assess credit risk. And the result is reasonable.

Probabilistic Lane-Change Decision-Making and Planning for Autonomous Heavy Vehicles

To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and passenger cars. This study proposes a probabilistic decision-making and trajectory planning framework for the autonomous heavy trucks. Firstly, the driving decision process is divided into intention generation and feasibility evaluations, which are realized using the utility theory and risk assessment, respectively. Subsequently the driving decision is made and sent to the trajectory planning module. In order to reflect the greater risks of the truck to other surrounding vehicles, the aggressiveness index(AI) is proposed and quantified to infer the asymmetrical risk level of lane-change maneuver. In the planning stage, the lateral and roll dynamics stability domains are developed as the constraints to exclude the candidate trajectories that would cause vehicle instability. Finally, the simulation results are compared between the proposed model and the artificial potential filed model in the scenarios extracted from the naturalistic driving data. It is shown that the proposed framework can provide the human-like lane-change decisions and truck-friendly trajectories, and performs well in dynamic driving environments.