scholarly journals Risk Assessment of Internet Credit Based on Big Data Analysis

2020 ◽  
Vol 214 ◽  
pp. 01012
Author(s):  
WANG HAORU ◽  
Yi Zhixuan ◽  
WEI YUJIA ◽  
Tianpeng Yao ◽  
Zhao Shuoheng ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Data Analysis ◽  
Credit Risk ◽  
Commercial Banks ◽  
Big Data Analysis ◽  
Financial Fraud ◽  
Credit Risk Assessment ◽  
New Business ◽  
Internet Finance

In recent years, network technology has continued to develop, and Internet finance has rapidly developed into a new business area. Internet credit is one of the important ways for banks to conduct business, and the scale of online credit has continued to expand. Due to the existence of various unpredictable factors, frequent emergencies, and online financial fraud, the overall market risk in the field of online credit has increased, and the rate of non-performing loans has continued to increase. Online financial fraud cases show that online credit risk has become one of the most prominent risks in the operation of commercial banks, which has a direct impact on the stability and development of commercial banks. We can build a bank database system based on big data, introduce professional big data analysis technical personnel, and constantly improve the big data sharing analysis platform, so that commercial banks can use system data more fully and effectively, and facilitate relevant business personnel to use big data technology for analysis and calculation. Big data is constantly produced, which provides basic materials for online credit risk assessment. Big data analysis technology is gradually mature, and it has the necessary conditions for online credit risk assessment. Based on the theories and technologies related to big data analysis, this paper comprehensively evaluates the online credit risk in the form of example data analysis, thereby effectively reducing the online credit risk coefficient.

scholarly journals Improved forecast assessment for the expected credit losses in credit risk monitoring in commercial banks in the context of international and Russian practices

2020 ◽  
Vol 15 (3) ◽  
pp. 445-457
Author(s):  
Elena Vladimirovna Travkina ◽  
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Credit Risk ◽  
Commercial Banks ◽  
Banking Sector ◽  
Assessment Model ◽  
Financial Instruments ◽  
Credit Risk Assessment ◽  
Ifrs 9 ◽  
Credit Losses

Current banking sector’s performance raises the issues connected with the IFRS 9 Financial Instruments driven transformation of the forecast assessment for the expected credit losses during monitoring and credit risk assessment in commercial banks. In this regard, it becomes important to conduct a comprehensive systematization of the existing Russian and international practices for monitoring and evaluating credit risk in commercial banks. The purpose of the study is to develop a comprehensive approach to the use of an effective model for the impairment of expected losses in banking activities. The novelty of the study includes the enhancement of the tools for the forecast assessment of the expected credit losses among the commercial banks’ clients to improve the credit risk management efficiency. The results from the implementation of IFRS 9 Financial Instruments in the banking area show that modern conditions maintain the uncertainty of the long-term impact of the credit risk on the commercial banks’ performance. What is more, a huge amount of additional information gives significant difficulties, which contributes into the sophisticated calculations of the future credit losses of the banks. It has been justified that a forecast assessment model for the expected credit losses of the clients during the monitoring and bank’s credit risk assessment should be based on the collective or individual ground. The efficient application of the expected losses impairment in the banking performance has been described as a fundamental tool to simulate the expected credit losses to provision for impairment. This model has been shown to be determined by the features of the credit activities and bank portfolio, types of its financial tools, sources of the available information, as well as the applied IT systems. The proposed model validation algorithm for the expected impairment losses could reduce the expected credit losses, decrease the volume of the created assessed reserves, as well as improve the overall commercial bank performance efficiency. Theoretically, the study develops the credit losses risk management in the context of the transformations in the global and Russian banking practices. From the perspective of the practical value, the research gives an opportunity to create an efficient forecast assessment model for the expected credit losses of the commercial banks’ clients, this model contributing into the cost effectiveness of the bank’s credit activities. A promising further research is considered to be aimed at developing the tools for the assessment of the commercial banks’ credit activity results in the context of the adopted changes connected with the introduction of IFRS 9 Financial Instruments in the Russian banking sector.

Credit Risk Management of Commercial Banks in Nepal

2019 ◽  
Vol 4 (1) ◽  
pp. 27-37
Author(s):  
Shreya Pradhan ◽  
Ajay K. Shah
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Credit Risk ◽  
Commercial Banks ◽  
Management Practices ◽  
Risk Mitigation ◽  
Mitigation Measures ◽  
Credit Risk Management ◽  
Loan Repayment ◽  
Credit Risk Assessment

The study is primarily focused on credit risk assessment practices in commercial banks on the basis of their internal efficiency, assessment of assets and borrower. The model of the study is based on the analysis of relationship between credit risk management practices, credit risk mitigation measures and obstacles and loan repayment. Based on a descriptive research approach the study has used survey-based primary data and performed a correlation analysis on them. It discovered that credit risk management practices and credit risk mitigation measures have a positive relationship with loan repayment, while obstacles faced by borrowers have no significant relationship with loan repayment. The study findings can provide good insights to commercial bank managers in analysing their model of credit risk management system, policies and practices, and in establishing a profitable and sustainable model for credit risk assessment, by setting a risk tolerance level and managing credit risks vis-a-vis the prevailing market competition.

scholarly journals Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Aiwen Niu ◽  
Bingqing Cai ◽  
Shousong Cai
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Credit Risk ◽  
Big Data Analytics ◽  
Assessment Model ◽  
Effective Control ◽  
Credit Score ◽  
Credit Risk Assessment ◽  
Index Weight ◽  
Online Lending

With the continuous development of big data technology, the data of online lending platform witness explosive development. How to give full play to the advantages of data, establish a credit risk assessment model, and realize the effective control of platform credit risk have become the focus of online lending platform. In view of the fact that the network loan data are mainly unbalanced data, the smote algorithm is helpful to optimize the model and improve the evaluation performance of the model. Relevant research shows that stochastic forest model has higher applicability in credit risk assessment, and cart, ANN, C4.5, and other algorithms are also widely used. In the influencing factors of credit evaluation, the weight of the applicant’s enterprise scale, working years, historical records, credit score, and other indicators is relatively high, while the index weight of marriage and housing/car production (loan) is relatively low.

scholarly journals Statistical Modeling and Simulation of Online Shopping Customer Loyalty Based on Machine Learning and Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jui-Chan Huang ◽  
Po-Chang Ko ◽  
Cher-Min Fong ◽  
Sn-Man Lai ◽  
Hsin-Hung Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Credit Risk ◽  
Customer Loyalty ◽  
Online Shopping ◽  
Big Data Analysis ◽  
Web Page ◽  
The Web

With the increase in the number of online shopping users, customer loyalty is directly related to product sales. This research mainly explores the statistical modeling and simulation of online shopping customer loyalty based on machine learning and big data analysis. This research mainly uses machine learning clustering algorithm to simulate customer loyalty. Call the k-means interactive mining algorithm based on the Hash structure to perform data mining on the multidimensional hierarchical tree of corporate credit risk, continuously adjust the support thresholds for different levels of data mining according to specific requirements and select effective association rules until satisfactory results are obtained. After conducting credit risk assessment and early warning modeling for the enterprise, the initial preselected model is obtained. The information to be collected is first obtained by the web crawler from the target website to the temporary web page database, where it will go through a series of preprocessing steps such as completion, deduplication, analysis, and extraction to ensure that the crawled web page is correctly analyzed, to avoid incorrect data due to network errors during the crawling process. The correctly parsed data will be stored for the next step of data cleaning or data analysis. For writing a Java program to parse HTML documents, first set the subject keyword and URL and parse the HTML from the obtained file or string by analyzing the structure of the website. Secondly, use the CSS selector to find the web page list information, retrieve the data, and store it in Elements. In the overall fit test of the model, the root mean square error approximation (RMSEA) value is 0.053, between 0.05 and 0.08. The results show that the model designed in this study achieves a relatively good fitting effect and strengthens customers’ perception of shopping websites, and relationship trust plays a greater role in maintaining customer loyalty.

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