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 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 Methodology for the validation of the credit scoring model of the retail portfolio

2021 ◽  
Vol 73 (7) ◽  
pp. 41-44
Author(s):  
Y.S. Zhieru
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Final Stage ◽  
Logistic Regression Model ◽  
Credit Scoring ◽  
Real Data ◽  
Scoring Model ◽  
Credit Scoring Model

The final stage of constructing a logistic regression model is checking its validity and testing it on real data. The degree of validity of a logistic regression model is evidenced by its ability to correctly classify borrowers, the model's ability to distinguish "good" borrowers from "bad" borrowers.

Towards Achieving Long-Term Debt Sustainability: A Systematic Review of the Key Determinants of Personal Bankruptcy

2021 ◽  
Vol 12 (3) ◽  
pp. 1305-1317
Author(s):  
Aqilah Nadiah MD SAHIQ Et.al
Keyword(s):  
Systematic Review ◽  
Financial Institutions ◽  
Social Stigma ◽  
Credit Scoring ◽  
Individual Characteristics ◽  
Personal Bankruptcy ◽  
Scoring Model ◽  
Meta Analyses ◽  
Credit Scoring Model

Purpose: This systematic review aimed to assess previous research about financial and non-financial causes of personal bankruptcy among individual households. Additionally, the paper aimed to provide an insight into the key determinants of personal bankruptcy and to determine their relationship with individual characteristics. The fundamental causes of bankruptcy and its effects on financial status were also discussed. Through understanding the causes of bankruptcy, we hope to help financial institutions to minimise the number of personal bankruptcies. Design/methodology/approach: A comprehensive systematic search was conducted to identify articles on determinants of personal bankruptcy. The selected articles were then analysed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. Findings: We identified several themes that emerged as the key determinants of personal bankruptcy filings. These determinants were demographic indicators, socioeconomic status indicators, debt indicators, financial indicators, social stigma indicators, behavioural indicators, and macroeconomic indicators. Research implications: The key determinants of personal bankruptcy that were identified in this systematic review are renowned factors in the personal bankruptcy literature. Therefore, these determinants should be studied extensively to examine their effects in other studies and using a different type of datasets. Practical implications: The findings of this study help the financial institutions to predict the likelihood of consumer default by developing an effective credit scoring model. Additionally, the development of an effective credit scoring model could serve as an early warning indicator to identify “high risk” client. Originality/value: Bankruptcy is a long-term process that does not occur instantly. Therefore, a longitudinal and comprehensive approach is required to understand bankruptcy. Our findings contribute to the current literature by providing a better understanding of the causes of personal bankruptcy. We recommend developing an effective credit scoring model to predict the likelihood of personal bankruptcy.  

Building a Credit Scoring Model Based on Data Mining Approaches

2020 ◽  
Vol 30 (02) ◽  
pp. 147-169
Author(s):  
Jasmina Nalić ◽  
Goran Martinovic
Keyword(s):  
Data Mining ◽  
High Performance ◽  
Banking Sector ◽  
Credit Scoring ◽  
Model Performance ◽  
Real Data ◽  
Support Vector ◽  
Scoring Model ◽  
Adopted Model ◽  
Credit Scoring Model

Nowadays, one of the biggest challenges in banking sector, certainly, is assessment of the client’s creditworthiness. In order to improve the decision-making process and risk management, banks resort to using data mining techniques for hidden patterns recognition within a wide data. The main objective of this study is to build a high-performance customized credit scoring model. The model named Reliable client is based on Bank’s real dataset and originally built by applying four different classification algorithms: decision tree (DT), naive Bayes (NB), generalized linear model (GLM) and support vector machine (SVM). Since it showed the greatest results, but also seemed as the most appropriate algorithm, the adopted model is based on GLM algorithm. The results of this model are presented based on many performance measures that showed great predictive confidence and accuracy, but we also demonstrated significant impact of data pre-processing on model performance. Statistical analysis of the model identified the most significant parameters on the model outcome. In the end, created credit scoring model was evaluated using another set of real data of the same Bank.

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