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.

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.

Fuzzy-Neuro Model for Intelligent Credit Risk Management

This paper presents hybrid fuzzy logic and neural network algorithm to solve credit risk management problem. Credit risk is the risk of loss due to a debtor’s non-payment of a loan or other line of credit. A method of evaluating the credit worthiness of a customer is complex and non-linear due to the diverse combinations of risk involve. To address this problem a credit scoring method is proposed in this paper using hybrid fuzzy logic-neural network (HFNN) model. The model will be implemented, tested, and validated for individual auto loans using real life bank data. The neural network is used as the learner and the fuzzy logic is used as the implementer. The neural network will fine tune the fuzzy sets, remove redundant input variables, and extract fuzzy rules. The extracted fuzzy rules are evaluated to retain the best k number of rules that will give final and intelligent decisions. The experiment results show that the perform-ance of the proposed HFNN model is very accurate, robust, and reliable. Comparison of these results to other previous published works is also presented in this paper.

Research on Tax Risk Regulation and Strategic Management in the Context of Big Data

With the rapid development of big data,big data has been more and more applied in all walks of life.Under the big data environment,massive big data provides convenience for regional tax risk control and strategic decision-making but also increases the difficulty of data supervision and management.By analyzing the status quo of big data and tax risk management,this paper finds many problems and puts forward effective countermeasures for tax risk supervision and strategic management by using big data.

Predictive Analytics for Project Risk Management Using Machine Learning

Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach for handling risks based on predictive analytics and machine learning (ML) that can work in real-time to help avoid risks and increase project adaptability. The main research aim of the study is to ascertain risk presence in projects by using historical data from previous projects, focusing on important aspects such as time, task time, resources and project results. t-SNE technique applies feature engineering in the reduction of the dimensionality while preserving important structural properties. This process is analysed using measures including recall, F1-score, accuracy and precision measurements. The results demonstrate that the Gradient Boosting Machine (GBM) achieves an impressive 85% accuracy, 82% precision, 85% recall, and 80% F1-score, surpassing previous models. Additionally, predictive analytics achieves a resource utilisation efficiency of 85%, compared to 70% for traditional allocation methods, and a project cost reduction of 10%, double the 5% achieved by traditional approaches. Furthermore, the study indicates that while GBM excels in overall accuracy, Logistic Regression (LR) offers more favourable precision-recall trade-offs, highlighting the importance of model selection in project risk management.

Sensitive Resource and Traffic Density Risk Analysis of Marine Spill Accidents Using Automated Identification System Big Data

This study developed a new methodology for analyzing the risk level of marine spill accidents from two perspectives,namely,marine traffic density and sensitive resources.Through a case study conducted in Busan,South Korea,detailed procedures of the methodology were proposed and its scalability was confirmed.To analyze the risk from a more detailed and microscopic viewpoint,vessel routes as hazard sources were delineated on the basis of automated identification system(AIS)big data.The outliers and errors of AIS big data were removed using the density-based spatial clustering of applications with noise algorithm,and a marine traffic density map was evaluated by combining all of the gridded routes.Vulnerability of marine environment was identified on the basis of the sensitive resource map constructed by the Korea Coast Guard in a similar manner to the National Oceanic and Atmospheric Administration environmental sensitivity index approach.In this study,aquaculture sites,water intake facilities of power plants,and beach/resort areas were selected as representative indicators for each category.The vulnerability values of neighboring cells decreased according to the Euclidean distance from the resource cells.Two resulting maps were aggregated to construct a final sensitive resource and traffic density(SRTD)risk analysis map of the Busan–Ulsan sea areas.We confirmed the effectiveness of SRTD risk analysis by comparing it with the actual marine spill accident records.Results show that all of the marine spill accidents in 2018 occurred within 2 km of high-risk cells(level 6 and above).Thus,if accident management and monitoring capabilities are concentrated on high-risk cells,which account for only 6.45%of the total study area,then it is expected that it will be possible to cope with most marine spill accidents effectively.

The Fifth China International Credit and Risk Management Conference

The 5th China International Credit and Risk Man- agement Conference was held on September 24- 26 in Xiamen,Fuiian province.More than 600 enterprises,mainly from the fields of import and export trade,manufacturing and finance,sent their repre-

Credit Risk Transfer and the Performance of Commercial Banks --Based on the Panel Data

Based on the panel data, we analyze the US commercial banks＇ CRT. According to the study, we find that the introduction of CRT will increase the level of banks＇ liquid risk. The performance of bank mainly is that its supervision and review of risk will drop, based on the impact of asymmetric information, commercial Banks transfer the bad loans to investors. Through the analysis we can see that after the transfer of credit risk in commercial bank did not increase income and reduce risk. Because commercial Banks can extend more bad loans to expand its lending scale, and bad loans will increase the bank overall risk.

The Application of Big data Mining in Risk Warning for Food Safety

Comprehensive evaluation and warning is very important and difficult in food safety. This paper mainly focuses on introducing the application of using big data mining in food safety warning field. At first,we introduce the concept of big data miming and three big data methods. At the same time,we discuss the application of the three big data miming methods in food safety areas. Then we compare these big data miming methods,and propose how to apply Back Propagation Neural Network in food safety risk warning.

Risk Analysis Technique on Inconsistent Interview Big Data Based on Rough Set Approach

Rough set theory is relativly new to area of soft computing to handle the uncertain big data efficiently. It also provides a powerful way to calculate the importance degree of vague and uncertain big data to help in decision making. Risk assessment is very important for safe and reliable investment. Risk management involves assessing the risk sources and designing strategies and procedures to mitigate those risks to an acceptable level. In this paper, we emphasize on classification of different types of risk factors and find a simple and effective way to calculate the risk exposure.. The study uses rough set method to classify and judge the safety attributes related to investment policy. The method which based on intelligent knowledge accusation provides an innovative way for risk analysis. From this approach, we are able to calculate the significance of each factor and relative risk exposure based on the original data without assigning the weight subjectively.

Analysis on the Risks of Health Information Platform under the Environment of Big Data

Big Data applications in the health service field have gradually been paid more close attention. Based on big data technology, more and more health information platforms are beginning to take effects, such as disease prevention, precision medicine, reducing expenditures for medical care and public health, improving medicine research and development. Meanwhile, the platforms have to face a lot of risks, such as health data disclosure, dispute of health data ownership, implicit contradiction explicit, unsustainable platform operation and so on. With the solutions of these risks, the construction of the public platform can provide better service for the citizens, hospital, pharmaceutical company, R＆D institutions or and other parties.

HOW BIG DATA MAKES CONSTRUCTION PROJECT RISK INTACT

Construction project is not a standalone engineering maneuver.It is closely linked to the well-being of local communities in concern.The city renovation in Beijing down center for Olympic 2008 transformed many antique architecture and regional landscape.It gave a world-recognized achievement in China s modem development and manifested a major milestone in China's economic development.In the course of metro construction projects,there are substantial interwoven municipal structures influencing the success of the projects,which including,but the least,all underground cables and ducts,sewage system,the power consumption of construction works,traffic diversion,air pollution,expatriate business activities and social security.There are many US and UK project insurance companies moving into Asia Pacific.They are doing re-insurance business on major construction guarantee,such as machinery damage,project on-time,power consumption,claims from contractors and communities.Environmental information,such as water quality,indoor and outdoor air quality,people inflow and lift waiting time play deterministic roles in construction's fit-touse.Big Data is a contemporary buzzword since 2013,and the key competence is to provide real time response to heuristic syndrome in order to make short-term prediction.This paper attempts to develop a conceptual model in big data for construction

Research on Influencing Factors and Countermeasures of Risk Control of State-Owned Enterprises under the Background of Big Data

This study explores the risk control and response strategies of state-owned enterprises in the context of big data.Global economic uncertainty poses new challenges to state-owned enterprises,necessitating innovative risk management approaches.This article proposes response strategies from four key aspects:establishing a proactive risk management culture,building a foundation in technology and data,conducting big data-driven risk analysis,and implementing predictive analysis and real-time monitoring.State-owned enterprises can foster a proactive risk management culture by cultivating employee risk awareness,demonstrating leadership,and establishing transparency and open communication.Additionally,data integration and analysis,leveraging the latest technology,are crucial factors that can help companies better identify risks and opportunities.

Using Python to Analyze Financial Big Data

As technology and the internet develop,more data are generated every day.These data are in large sizes,high dimensions,and complex structures.The combination of these three features is the“Big Data”[1].Big data is revolutionizing all industries,bringing colossal impacts to them[2].Many researchers have pointed out the huge impact that big data can have on our daily lives[3].We can utilize the information we obtain and help us make decisions.Also,the conclusions we drew from the big data we analyzed can be used as a prediction for the future,helping us to make more accurate and benign decisions earlier than others.If we apply these technics in finance,for example,in stock,we can get detailed information for stocks.Moreover,we can use the analyzed data to predict certain stocks.This can help people decide whether to buy a stock or not by providing predicted data for people at a certain convincing level,helping to protect them from potential losses.

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.

Randomized Constraint Limit Linear Programming in Risk Management

Traditional linear program (LP) models are deterministic. The way that constraint limit uncertainty is handled is to compute the range of feasibility. After the optimal solution is obtained, typically by the simplex method, one considers the effect of varying each constraint limit, one at a time. This yields the range of feasibility within which the solution remains feasible. This sensitivity analysis is useful for helping the analyst get a feel for the problem. However, it is unrealistic because some constraint limits can vary randomly. These are typically constraint limits based on expected inventory. Inventory may fall short if there are overdue deliveries, unplanned machine failure, spoilage, etc. A realistic LP is created for simultaneously randomizing the constraint limits from any probability distribution. The corresponding distribution of objective function values is created. This distribution is examined directly for central tendencies, spread, skewness and extreme values for the purpose of risk analysis. The spreadsheet design presented is ideal for teaching Monte Carlo simulation and risk analysis to graduate students in business analytics with no specialized programming language requirement.

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.

Consumer Credit Risk Management in an Emerging Market: The Case of China

With the liberalization of the financial service sector mandated by China＇s access to the WTO, China＇s credit card market has received a great deal of attention from global financial institutions. This paper examines the enormous growth opportunities and key barriers facing the development of the credit card industry in China, and discusses the importance and tools of consumer credit risk management. In the process of rapid expansion of China＇s consumer credit card industry, credit risk management should be treated as a top priority to avoid a pile up of bad debt in credit card receivables. This requires the development of an updated and comprehensive national consumer credit database and the use of credit risk modeling and scoring in predicting consumer behavior. As structured finance develops in China, the securitization of credit card receivables into asset-backed securities might also serve as an alternative to traditional credit risk management.

Credit risk prediction for small and medium enterprises utilizing adjacent enterprise data and a relational graph attention network

Credit risk prediction for small and medium enterprises(SMEs)has long posed a complex research challenge.Traditional approaches have primarily focused on enterprise-specific variables,but these models often prove inadequate when applied to SMEs with incomplete data.In this innovative study,we push the theoretical boundaries by leveraging data from adjacent enterprises to address the issue of data deficiency.Our strategy involves constructing an intricate network that interconnects enterprises based on shared managerial teams and business interactions.Within this network,we propose a novel relational graph attention network(RGAT)algorithm capable of capturing the inherent complexity in its topological information.By doing so,our model enhances financial service providers'ability to predict credit risk even in the face of incomplete data from target SMEs.Empirical experiments conducted using China's SMEs highlight the predictive proficiency and potential economic benefits of our proposed model.Our approach offers a comprehensive and nuanced perspective on credit risk while demonstrating the advantages of incorporating network-wide data in credit risk prediction.

Credit Risk Prediction Based on Improved ADASYN Sampling and Optimized LightGBM

A credit risk prediction model named KM-ADASYN-TL-FLLightGBM(KADT-FLightGBM)is proposed in this study.Firstly,to overcome the limitation of traditional sampling methods in dealing with imbalanced datasets,an improved ADASYN sampling with K-means clustering algorithm is constructed.Moreover,the Tomek Links method is used to filter the generated samples.Secondly,an utilized an optimized LightGBM algorithm with the Focal Loss is employed to training the model using the datasets obtained by the improved ADASYN sampling.Finally,the comparative analysis between the ensemble model and other different sampling methodologies is conducted on the Lending Club dataset.The results demonstrate that the proposed model effectively minimizes the misclassification of minority classes in credit risk prediction and can be used as a reference for similar studies.