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.

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.

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.

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.

A dynamic credit risk assessment model with data mining techniques:evidence from Iranian banks

Giving loans and issuing credit cards are two of the main concerns of banks in that they include the risks of non-payment.According to the Basel 2 guidelines,banks need to develop their own credit risk assessment systems.Some banks have such systems;nevertheless they have lost a large amount of money simply because the models they used failed to accurately predict customers’defaults.Traditionally,banks have used static models with demographic or static factors to model credit risk patterns.However,economic factors are not independent of political fluctuations,and as the political environment changes,the economic environment evolves with it.This has been especially evident in Iran after the 2008-2016 USA sanctions,as many previously reliable customers became unable to repay their debt(i.e.,became bad customers).Nevertheless,a dynamic model that can accommodate fluctuating politicoeconomic factors has never been developed.In this paper,we propose a model that can accommodate factors associated with politico-economic crises.Human judgement is removed from the customer evaluation process.We used a fuzzy inference system to create a rule base using a set of uncertainty predictors.First,we train an adaptive network-based fuzzy inference system(ANFIS)using monthly data from a customer profile dataset.Then,using the newly defined factors and their underlying rules,a second round of assessment begins in a fuzzy inference system.Thus,we present a model that is both more flexible to politico-economic factors and can yield results that are max compatible with real-life situations.Comparison between the prediction made by proposed model and a real non-performing loan indicates little difference between them.Credit risk specialists also approve the results.The major innovation of this research is producing a table of bad customers on a monthly basis and creating a dynamic model based on the table.The latest created model is used for assessing customers henceforth,so the whole process of customer assessment need not be repeated.We assert that this model is a good substitute for the static models currently in use as it can outperform traditional models,especially in the face of economic crisis.

Credit risk evaluation using adaptive Lq penalty SVM with Gauss kernel

In order to improve the performance of support vector machine (SVM) applications in the field of credit risk evaluation, an adaptive Lq SVM model with Gauss kernel (ALqG-SVM) is proposed to evaluate credit risks. The non-adaptive penalty of the object function is extended to (0, 2] to increase classification accuracy. To further improve the generalization performance of the proposed model, the Gauss kernel is introduced, thus the non-linear classification problem can be linearly separated in higher dimensional feature space. Two UCI credit datasets and a real life credit dataset from a US major commercial bank are used to check the efficiency of this model. Compared with other popular methods, satisfactory results are obtained through a novel method in the area of credit risk evaluation. So the new model is an excellent choice.

Research on the Model of Household Credit Risk Evaluation of Rural Microfinance

Since rural microfinance is a credit which grants loans without collateral and guarantees to farmers,it is considerably important to evaluate and control the household credit risk.Through establishing the evaluation index system and then using catastrophe progression theory,three common types of catastrophe system and the normalization formula,we get the comprehensive evaluation.Finally,we take the empirical test and the result shows that this method is simpler and more objective which can be used by the credit cooperatives to decide whether to authorize the loans.

Credit Risk Model Taking Account of Inflation and Its Contribution to Macroeconomic Discussion on Effect of Inflation on Output Growth

We use Extended Merton model(EMM)for estimating the firm’s credit risks in the presence of inflation.We show quantitatively that inflation is an influential factor making either a benign or adverse effect on the firm’s survival,supporting at the microeconomic level New Keynesian findings of the nonlinear inflation effect on output growth.Lower inflation increasing the firm’s expected rate of return can raise its mean year returns and decrease its default probability.Higher inflation,decreasing the expected rate return,makes the opposite effect.The magnitude of the adverse effect depends on the firm strength:for a steady firm,this effect is small,whereas for a weaker firm,it can be fatal.EMM is the only model taking account of inflation.It can be useful for banks or insurance companies estimating credit risks of commercial borrowers over the debt maturity,and for the firm’s management planning long-term business operations.

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.

Credit risk constraint mechanisms in rural financial reform

To research the operating mechanisms of rural financial reform, through setting up a contract model, the constraint roles of reputation and legal intervention on the default risk arising in the operating of the credit union funds are inspected. Analysis indicates that the increase in reputation cost can reduce the probability of union member default behavior and the probability of turning to the law for the credit union funds. Meanwhile, the amount of loans and the interest rates can increase the probability of turning to the law for the credit union funds. Below the marginal values, the penalty mechanisms can reduce the balancing probabilities of member default behavior and turning to the law for the credit union funds, namely, the penalty has some "substitution effect" for turning to the law for the credit union funds.

Clues from networks:quantifying relational risk for credit risk evaluation of SMEs

Owing to information asymmetry,evaluating the credit risk of small-and mediumsized enterprises(SMEs)is difficult.While previous studies evaluating the credit risk of SMEs have mostly focused on intrinsic risk generated by SMEs,our study considers both intrinsic and relational risks generated by neighbor firms’publicly available risk events.We propose a framework for quantifying relational risk based on publicly available risk events for SMEs’credit risk evaluation.Our proposed framework quantifies relational risk by weighting the impact of publicly available risk events of each firm in an interfirm network—considering the impact of interfirm network type,risk event type,and time dependence of risk events—and combines the relational risk score with financial and demographic features to evaluate SMEs credit risk.Our results reveal that relational risk score significantly improves both discrimination and granting performances of credit risk evaluation of SMEs,providing valuable managerial and practical implications for financial institutions.

Impact of risk management strategies on the credit risk faced by commercial banks of Balochistan

This study aims to identify risk management strategies undertaken by the commercial banks of Balochistan,Pakistan,to mitigate or eliminate credit risk.The findings of the study are significant as commercial banks will understand the effectiveness of various risk management strategies and may apply them for minimizing credit risk.This explanatory study analyses the opinions of the employees of selected commercial banks about which strategies are useful for mitigating credit risk.Quantitative data was collected from 250 employees of commercial banks to perform multiple regression analyses,which were used for the analysis.The results identified four areas of impact on credit risk management(CRM):corporate governance exerts the greatest impact,followed by diversification,which plays a significant role,hedging and,finally,the bank’s Capital Adequacy Ratio.This study highlights these four risk management strategies,which are critical for commercial banks to resolve their credit risk.

The Default Risk Management in Financial Institutions： Case of the Credit Risk in Lebanese MFIs

In Lebanon, as in some countries, the major financial institutions in the economy experienced by the country in the aftermath of independence were banks and insurance companies. However, the operation of these financial institutions obeys to some requirements that are not often likely to allow economic agents with low purchasing power to obtain the necessary funds to finance their production activities. Microfinance therefore comes in as the beginning of seeking effective market oriented solutions to the provision of substantial and effective financial resource for poor groups of people who do not have access to financial service from formal government and private financial institution. Microfinance Institutions （MFIs） are created for a social and sometimes non-profit objective. In Lebanon, many limitations hinder the development of MFIs including the lack of regulations, economic conditions, insecurity, political conflict, financial resources, and the risk of interest rates. Microfinance in Lebanon saw the light during the 1975-1990 Civil War through programs of charitable and community organizations, and really started to develop only in the second half of the 1990s. Capping interest rates may affect the access of poor people to financial services. The problem is that the granting of very small loans involves inevitably higher administrative costs than those offered by traditional bank loans. Therefore, MFIs that seek profitability should have higher interest rates than those charged by traditional banks. By providing money to poor people, how do MFIs in Lebanon reduce the credit risk？ This theme＇s treatment requires a qualitative analysis development. Indeed, after the selection of a representative sample, semi-structured interviews were done with the MFIs managers, and several researches done on this topic were analyzed. The data obtained from the above are treated by the triangulation of different data and the interviews analysis by the method of discourse content analysis. In addition, a literature review was done through scientific journals, books, newspapers, and websites.

Role of National Culture of Foreign Investors in the Area of Credit Risk Management： Case Study of Polish Banking Industry

Among the researches dedicated to the risk management in banks, there are not many analyses made from cultural point of view. The author attempts to assess the attitude to credit risk in the Polish banking system, in terms of cultural factors influencing the approach to this issue. The purpose of this paper is focused on testing the hypothesis that foreign owners of banks （headquarters） transfer elements of their national culture to its subsidiaries operating in Poland. It is done by analysis of statistical correlations between the indexes defining the main characteristics of national cultures and the actual financial performance indicators reached by selected banks in the period from 2004 to 2010 in Poland. The study objectives are the following： Firstly, whether the owners from different countries transfer their cultural attitude to risk to subordinate daughter-banks in Poland. The second question concerns the relevance of uncertainty avoidance and individualism/collectivism concepts of two, to some extent, competing approaches. The findings say that the regularity of the transfer of the cultural attitude to credit risk from the parent banks to their subsidiaries is confirmed by interviews with senior managers, but it is only partially reflected in the statistics. Main outcomes of the study propose that the cultural factors of bank risk management policy shouldn＇t be ignored and developed in other cross-cultural research areas, e.g. ethnocentrism. Generally, these studies are present lessons for companies, investors, and policymakers, but the usefulness of these implications varies.

The Relationship Between Credit Risk Management and Profitability Among the Commercial Banks in Kenya

Banks operate in an environment of considerable risks and uncertainty. Credit risk has always been a vicinity of concern not only to bankers but to all in the business world because the risks of a trading partner not fulfilling his obligations in full on due date can seriously jeopardize the affairs of the other partner. Credit risk management in banks has become more important not only because of the series of financial crisis that the world has experienced in the recent past, but also the introduction of Basel II Accord. The objective of the study was to establish the relationship between credit risk management and profitability in commercial banks in Kenya, Both qualitative and quantitative methods were used in order to fulfill the main purpose of the study. A regression model was used to do the empirical analysis. The results obtained from the regression model show that there is an effect of credit risk management on profitability at a reasonable level. The findings and analysis reveal that credit risk management has an effect on profitability in all the commercial banks analyzed.

Research on Personal Credit Risk Assessment Model Based on Instance-Based Transfer Learning

Personal credit risk assessment is an important part of the development of financial enterprises. Big data credit investigation is an inevitable trend of personal credit risk assessment, but some data are missing and the amount of data is small, so it is difficult to train. At the same time, for different financial platforms, we need to use different models to train according to the characteristics of the current samples, which is time-consuming. In view of these two problems, this paper uses the idea of transfer learning to build a transferable personal credit risk model based on Instance-based Transfer Learning (Instance-based TL). The model balances the weight of the samples in the source domain, and migrates the existing large dataset samples to the target domain of small samples, and finds out the commonness between them. At the same time, we have done a lot of experiments on the selection of base learners, including traditional machine learning algorithms and ensemble learning algorithms, such as decision tree, logistic regression, xgboost and so on. The datasets are from P2P platform and bank, the results show that the AUC value of Instance-based TL is 24% higher than that of the traditional machine learning model, which fully proves that the model in this paper has good application value. The model’s evaluation uses AUC, prediction, recall, F1. These criteria prove that this model has good application value from many aspects. At present, we are trying to apply this model to more fields to improve the robustness and applicability of the model;on the other hand, we are trying to do more in-depth research on domain adaptation to enrich the model.

An Evaluation of the Effect of Credit Risk Management （CRM） on the Profitability of Nigerian Banks

This paper assesses the effect of credit risk management （CRM） on the profitability of Nigerian banks with a view to discovering the extent to which default rate （DR）, cost per loan asset （CLA）, and capital adequacy ratio （CAR） influence return on asset （ROA） as a measure of banks＇ profitability. Data were generated from secondary sources, specifically, the annual reports and accounts of quoted banks from 2002 to 2011. Descriptive statistics, correlation, as well as random-effect generalized least square （GLS） regression techniques were utilized as tools of analysis in the study. The findings establish that CRM as measured by three independent variables has a significant positive effect on the profitability of Nigerian banks as indicated by the coefficient of determinations ＂R2 value＂ which shows the within and between values of 40.89% and 58.35% （which are impressive） while the overall R2 iS 43.91%, indicating that the variables considered in the model account for about 44% change in the dependent variable, that is, profitability. The study recommends that banks＇ management should be more scientific （application of risk evaluation techniques） in their credit risk assessment and management of loan portfolios in order to minimize the high incidence of non-performing loans and their negative effect on profitability.

Determination of Credit Risk Charges for Malaysian Life Insurance Industry： An Application of Default Probability

The objective of this paper is to measure the risk charge for credit risk as one of the components in the risk based capital of the capital adequacy framework. Currently, the risk charge for credit risk is measured by referring it to the credit rating of a company. Following the subprime crisis in 2007, the markets start to question the soundness of the credit rating issued as it has resulted in an inadequate risk charge. Therefore, this study attempts to determine the risk charge for credit risk using the probability of default （PD） for life insurers in Malaysia. The credit risk has been categorized into several types of debt obligations. Whereby, the KMV-Merton model has been used to measure the distance to default and estimate the probability of default. The estimation of default probability is based on the movement in the price index of several debt obligations. The price index of debt obligations from year 2004 to 2009 is collected inclusive of the subprime crisis period during the crisis period. Therefore, Malaysia insurance industry is The results found that the risk charges are lower not affected by the subprime crisis in 2007.

Credit Risk （Based on Analysis of Financial Statements） as the Decisive Factor Influencing the Risk of Investors

Banks as the key subjects in the financing of investment have a strong influence on the risk of investors. Hence, the solvency of the bank is of crucial importance for the risk management in the investment process. Given the fact of underdevelopment of financial markets and the lack of trading activities in securities, it is evident that the investments of banks in the developing countries mostly include lending investments. Looking at the key categories of risk that influence the overall risk of the banking business in such conditions, it can be concluded that credit risk presents the dominant and decisive factor. The aim of the paper is to select the bank determinant key factors of credit risk and to determine the extent to which non-performing loans （NPL） of bank credits affect the solvency of banks, and therefore also the risk of investors. This selection of the main determinants will be based on the analysis of financial statements. This is essential, especially taking into account the impact of the global financial crisis and the increasingly frequent falling into insolvency customers. Finally, liquidity of customers is that of the bank, and it is crucial for investors to timely identify possible risks associated with bank loans in order to proactively manage risk investment.