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 Credit concession through credit scoring: Analysis and application proposal

2017 ◽  
Vol 13 (1) ◽  
pp. 51 ◽  
Author(s):  
Oriol Amat ◽  
Raffaele Manini ◽  
Marcos Antón Renart
Keyword(s):  
Financial Institutions ◽  
Design Methodology ◽  
Credit Scoring ◽  
Real Data ◽  
Statistical Techniques ◽  
Probability Of Default ◽  
Relative Importance ◽  
Linear Discriminant ◽  
Scoring Model ◽  
Credit Scoring Model

Purpose: The study herein develops and tests a credit scoring model which can help financial institutions in assessing credit requests. Design/methodology/approach: The empirical study has the objective of answering two questions: (1) Which ratios better discriminate the companies based on their being solvent or insolvent? and (2) What is the relative importance of these ratios? To do this, several statistical techniques with a multifactorial focus have been used (Multivariate Analysis of Variance, Linear Discriminant Analysis, Logit and Probit Models). Several samples of companies have been used in order to obtain and to test the model. Findings: Through the application of several statistical techniques, the credit scoring model has been proved to be effective in discriminating between good and bad creditors. Research limitations:  This study focuses on manufacturing, commercial and services companies of all sizes in Spain; Therefore, the conclusions may differ for other geographical locations.Practical implications:  Because credit is one of the main drivers of growth, a solid credit scoring model can help financial institutions assessing to whom to grant credit and to whom not to grant credit.Social implications: Because of the growing importance of credit for our society and the fear of granting it due to the latest financial turmoil, a solid credit scoring model can strengthen the trust toward the financial institutions assessment’s. Originality/value: There is already a stream of literature related to credit scoring. However, this paper focuses on Spanish firms and proves the results of our model based on real data. The application of the model to detect the probability of default in loans is original.

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 Research on Risk Assessment Method of Enterprise's Arrears Based on Block-Chain Credit Consensus

2021 ◽  
Author(s):  
Qian Zhang ◽  
Sujie Xing
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Power Supply ◽  
Credit Scoring ◽  
Assessment Method ◽  
Scoring Model ◽  
Blockchain Technology ◽  
Risk Assessment Method ◽  
Credit Data ◽  
Credit Scoring Model

Abstract Background:At present, the power supply market has always acquiesced to the rule of ‘use electricity first, pay later’,some electricity users may delay payment or even default on electricity bills due to various reasons, causing problems such as a long recovery period for electricity bills and difficulty in debt settlement. In order to reduce this kind of phenomenon, the power supply company must understand the user's historical electricity consumption data, capital status, credit status and other information, but the establishment of such a database requires a lot of time and human resource costs.Methods:Based on the distributed storage technology of blockchain, this paper abstracts power supply companies, power users, banking financial institutions, government regulatory agencies, etc. into nodes on the alliance chain.After that,this paper introduces a credit scoring model to judge the credit rating of user information based on characteristic indicators, and select the corresponding electricity fee recovery policy after the result is obtained, so as to reduce the operating risk of the power supply company.Results:This article combines the power and energy market with blockchain technology to establish a secure and distributed credit data blockchain, and at the same time establish a credit scoring model based on expert review questionnaire data. The analysis results show that this mechanism is suitable for credit data storage and sharing in energy transactions. Conclusions:Research and analysis indicate that the credit risk assessment method of electricity transaction data proposed in this article provides a theoretical basis for the combination of electricity transaction credit risk assessment and blockchain technology,which will help improve the company's ability to assess the risk of arrears and reduce the operating risk of power supply companies.

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 Modeling with ontologies design patterns: credit scorecard as a case study

2020 ◽  
Vol 17 (1) ◽  
pp. 429
Author(s):  
Jalil Elhassouni ◽  
Abderrahim El Qadi ◽  
Mehdi Bazzi ◽  
Mohamed El Haziti
Keyword(s):  
Credit Risk ◽  
Design Patterns ◽  
Domain Knowledge ◽  
Credit Scoring ◽  
Decision Making Process ◽  
Credit Risk Management ◽  
Scoring Model ◽  
Ontology Design ◽  
Credit Scoring Model

<span lang="EN-US">This paper proposes an ontological scorecard model for credit risk management. The purpose of credit scoring model is to reduce the possibility of potential losses with regard to issued loans. Loans are provided according to strict criteria which contain information about the client, loan structure, the purpose, repayment source and collateral. Several techniques have been used for credit risk assessment before granting a loan. Ontology design patterns is used here to enable the implementation of domain knowledge using the OWL rules and to improve the decision making process in credit monitoring. The modeling of our ontology will make the data publication simpler and graph structures intuitive, thus making its reusability and expandability easier.</span>

A novel multi-stage ensemble model for credit scoring based on synthetic sampling and feature transformation

2021 ◽  
pp. 1-16
Author(s):  
Fang He ◽  
Wenyu Zhang ◽  
Zhijia Yan
Keyword(s):  
Credit Scoring ◽  
Imbalanced Data ◽  
Transformation Method ◽  
Classification Performance ◽  
Ensemble Model ◽  
Feature Transformation ◽  
Learning Methods ◽  
Scoring Model ◽  
Multi Stage ◽  
Credit Scoring Model

Credit scoring has become increasingly important for financial institutions. With the advancement of artificial intelligence, machine learning methods, especially ensemble learning methods, have become increasingly popular for credit scoring. However, the problems of imbalanced data distribution and underutilized feature information have not been well addressed sufficiently. To make the credit scoring model more adaptable to imbalanced datasets, the original model-based synthetic sampling method is extended herein to balance the datasets by generating appropriate minority samples to alleviate class overlap. To enable the credit scoring model to extract inherent correlations from features, a new bagging-based feature transformation method is proposed, which transforms features using a tree-based algorithm and selects features using the chi-square statistic. Furthermore, a two-layer ensemble method that combines the advantages of dynamic ensemble selection and stacking is proposed to improve the classification performance of the proposed multi-stage ensemble model. Finally, four standardized datasets are used to evaluate the performance of the proposed ensemble model using six evaluation metrics. The experimental results confirm that the proposed ensemble model is effective in improving classification performance and is superior to other benchmark models.

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