scholarly journals РАЗРАБОТКА СКОРИНГОВОЙ МОДЕЛИ ОЦЕНКИ КРЕДИТНОГО РИСКА

2021 ◽  
Vol 7 (3(57)) ◽  
pp. 45-47
Author(s):  
ЭЛДОР АНВАРОВИЧ НОЗИМОВ
Keyword(s):  
Discriminant Analysis ◽  
Credit Risk ◽  
Factor Space ◽  
Scoring Model ◽  
Potential Client

Исследовалась кредитоспособность заемщиков на основе клиентской базы банка. Было сформировано факторное пространство, позволяющее формализовать и объяснить различия между классами прежних заемщиков. Выявлены факторы, вносящие наибольший вклад в различение классов. В результате проведения дискриминантного анализа разработана скоринговая модель оценки кредитного риска, позволяющая адекватно и быстро оценить кредитоспособность потенциального клиента банка, что способствует росту конкурентоспособности на рынке кредитования. The creditworthiness of borrowers was investigated on the basis of the bank's client base. A factor space was formed to formalize and explain the differences between the classes of former borrowers. The factors that make the greatest contribution to the differentiation of classes are revealed. As a result of the discriminant analysis, a scoring model for assessing credit risk was developed, which makes it possible to adequately and quickly assess the creditworthiness of a potential client of the bank, which contributes to the growth of competitiveness in the lending market.

scholarly journals Design and Development of Credit Scoring Model for Conventional Banks for Individual Borrowing Case Study on PT BPR Sungai Puar District Agam

2018 ◽  
Vol 1 (1) ◽  
pp. 43-56
Author(s):  
Rio Hendriadi ◽  
Anne Putri ◽  
Dona Amelia ◽  
Rany Syafrina
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Credit Risk ◽  
Credit Scoring ◽  
Scoring Model ◽  
Secondary Sources ◽  
Conventional Banks ◽  
Credit Scoring Model ◽  
Conventional Bank

Objective – This research is conducted to design and to develop credit scoring model on conventional bank in order to determine individual loan, the research takes place in PT BPR Sungai Puar, Kabupaten Agam. This model tries to evaluate the credit risk of BPR Sungai Puar.Design/methodology – The data are considered as secondary sources as they are taken from BPR Sungai Puar database by classifying them into two analysis tools including discriminant analysis and logistic regression. Results – The resuts are presentes inform of model and credit scoring perfection on PT BPR Sungai Puar Kabupaten Agam.Keywords Credit Scoring Model, Conventional Banks, Individual Loan

scholarly journals Measuring and Managing Credit Risk for Chinese Microfinance Institutions

2018 ◽  
Vol 10 (7) ◽  
pp. 56
Author(s):  
Jie Li ◽  
Zhenyu Sheng
Keyword(s):  
Credit Risk ◽  
Predictive Power ◽  
Direct Method ◽  
Credit Scoring ◽  
Microfinance Institutions ◽  
Linear Discriminant ◽  
Management Level ◽  
Scoring Model ◽  
Subjective Appraisals ◽  
Credit Scoring Model

Chinese microfinance institutions need to measure and manage credit risk in a quantitative way in order to improve competitiveness. To establish a credit scoring model (CSM) with sound predictive power, they should examine various models carefully, identify variables, assign values to variables and reduce variable dimensions in an appropriate way. Microfinance institutions could employ both CSM and loan officer’s subjective appraisals to improve risk management level gradually. The paper sets up a CSM based on the data of a microfinance company running from October 2009 to June 2014 in Jiangsu province. As for establishing the model, the paper uses Linear Discriminant Analysis (LDA) method, selects 16 initial variables, employs direct method to assign variables and adopts all the variables into the model. Ten samples are constructed by randomly selecting records. Based on the samples, the coefficients are determined and the final none-standardized discriminant function is established. It is found that Bank credit, Education, Old client and Rate variables have the greatest impact on the discriminant effect. Compared with the same international models, this model’s classification effect is fine. The paper displays the key technical points to build a credit scoring model based on a practical application, which provides help and references for Chinese microfinance institutions to measure and manage credit risk quantitatively.

scholarly journals A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Pranith Kumar Roy ◽  
Krishnendu Shaw
Keyword(s):  
Credit Risk ◽  
Financial Institutions ◽  
Credit Scoring ◽  
Real Life ◽  
Financial Data ◽  
Loan Pricing ◽  
Scoring Model ◽  
Record Keeping ◽  
Return On Capital ◽  
Credit Scoring Model

AbstractSmall- and medium-sized enterprises (SMEs) have a crucial influence on the economic development of every nation, but access to formal finance remains a barrier. Similarly, financial institutions encounter challenges in the assessment of SMEs’ creditworthiness for the provision of financing. Financial institutions employ credit scoring models to identify potential borrowers and to determine loan pricing and collateral requirements. SMEs are perceived as unorganized in terms of financial data management compared to large corporations, making the assessment of credit risk based on inadequate financial data a cause for financial institutions’ concern. The majority of existing models are data-driven and have faced criticism for failing to meet their assumptions. To address the issue of limited financial record keeping, this study developed and validated a system to predict SMEs’ credit risk by introducing a multicriteria credit scoring model. The model was constructed using a hybrid best–worst method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Initially, the BWM determines the weight criteria, and TOPSIS is applied to score SMEs. A real-life case study was examined to demonstrate the effectiveness of the proposed model, and a sensitivity analysis varying the weight of the criteria was performed to assess robustness against unpredictable financial situations. The findings indicated that SMEs’ credit history, cash liquidity, and repayment period are the most crucial factors in lending, followed by return on capital, financial flexibility, and integrity. The proposed credit scoring model outperformed the existing commercial model in terms of its accuracy in predicting defaults. This model could assist financial institutions, providing a simple means for identifying potential SMEs to grant credit, and advance further research using alternative approaches.

scholarly journals Improved ML-Based Technique for Credit Card Scoring in Internet Financial Risk Control

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Shuangshuang Fan ◽  
Yanbo Shen ◽  
Shengnan Peng
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Credit Card ◽  
Financial Risk ◽  
Risk Control ◽  
Assessment Model ◽  
Complex Data ◽  
Continuous Growth ◽  
Scoring Model ◽  
Extreme Gradient Boosting

With the rapid development of China’s Internet finance industry and the continuous growth of transaction amount in recent years, a variety of financial risks have increased, especially credit risk in the financial industry. Also, the credit risk evaluation is usually made by using the application card scoring model, which has the shortcomings of strict data assumption and inability to process complex data. In order to overcome the limitations of the credit card scoring model and evaluate credit risk better, this paper proposes a credit evaluation model based on extreme gradient boosting tree (XGBoost) machine learning (ML) algorithm to construct a credit risk assessment model for Internet financial institutions. At the same time, an Internet lending company in China is taken as a case study to compare the performance of the traditional credit card scoring model and the proposed machine learning (ML) algorithm model. The results show that ML algorithm has a very significant advantage in the field of Internet financial risk control, it has more accurate prediction results and has no particularly strict assumptions and restrictions on data, and the process of processing data is more convenient and reliable. We should increase the application of ML in the field of financial risk control. The value of this paper lies in enriching the related research of financial technology and providing a new reference for the practice of financial risk control.

Discriminant Analysis and Logistic Regression Applying To Credit Risk Management

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Ngongo Isidore ◽  
◽  
Etoua Magloire ◽  
Jimbo Claver ◽  
Mengue Mvondo Jenner ◽  
...  
Keyword(s):  
Risk Management ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Financial Crisis ◽  
Credit Risk ◽  
Efficient Method ◽  
Credit Risk Management ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
The World

The financial crisis that is currently shaking the world, particularly the successive failures of the major banks have brought the issue of banking risks, including credit risk, back to the forefront. This risk must now be managed by more sophisticated methods. In this paper we present two methods that allow us to establish two functions, namely Fisher discriminant analysis and logistic regression; these two functions allow us to evaluate the risk of non-repayment incurred by a bank in the light of our data. It emerges that Fisher discriminant analysis is more effective or efficient than logistic regression for the evaluation of the risk of non-repayment of credit. Discriminant analysis and logistic regression are two methods of credit risk management here the problem we are trying to solve is how to help banks choose the most efficient method between the latter two.

scholarly journals Die voorspelling van kredietrisiko met behulp van biografiese veranderlikes: Is dit die antwoord?

1983 ◽  
Vol 14 (4) ◽  
pp. 166-171
Author(s):  
P. L.S. Ackermann ◽  
W. P. Jansen van Rensburg
Keyword(s):  
Regression Analysis ◽  
Discriminant Analysis ◽  
Credit Risk ◽  
Representative Sample ◽  
Stepwise Regression ◽  
Stepwise Regression Analysis ◽  
Stepwise Discriminant Analysis ◽  
Relative Importance ◽  
Multiple Stepwise Regression Analysis

The prediction of credit risk by means of biographic variables: Is this the answer? The objective of this study is to identify specific biographical variables, to quantity them and to investigate their relative importance in the prediction of credit risk. A representative sample of 250 bad credit risk clients and 250 good credit risk clients is used in the study. A multiple stepwise regression analysis and multiple stepwise discriminant analysis were carried out. Nine biographical variables were identified which explain approximately 16% of the variance of credit risk.

scholarly journals Credit risk assessment of agricultural enterprises in the Republic of Serbia: Logistic regression vs discriminant analysis

2021 ◽  
Vol 68 (4) ◽  
pp. 881-894
Author(s):  
Dragana Tekić ◽  
Beba Mutavdžić ◽  
Dragan Milić ◽  
Nebojša Novković ◽  
Vladislav Zekić ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Credit Risk ◽  
Regression Model ◽  
Financial Stability ◽  
Logistic Regression Model ◽  
Agricultural Enterprises ◽  
Credit Risk Assessment ◽  
The Republic

Credit risk assessment of agricultural enterprises in the Republic of Serbia was analyzed in this research by applying discriminant analysis and logistic regressions. The aim of the research is to determine the financial indicators which financial analysts consider when analyzing a loan application that have the most influence on the decision to approve or reject a loan application. The internal determinants of credit risk of agricultural enterprises are analyzed, i.e., indicators of financial leverage, profitability, liquidity, solvency, financial stability and effectiveness. The analyzed models gave different results in significance of the observed indicators. The indicators that stood out as significant in both models are only indicators of profitability and solvency. The model of discriminant analysis has successfully classified rate 81.0%, while the logistic regression model has successfully classifies rate 89.8%. In modeling the credit risk of agricultural enterprises in the Republic of Serbia, the logistic regression model gives better results.

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