Understanding the impact of borrowers' behavioural and psychological traits on credit default: review and conceptual model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Akanksha Goel ◽  
Shailesh Rastogi
Keyword(s):  
Conceptual Model ◽  
Credit Scoring ◽  
Psychological Traits ◽  
Content Type ◽  
Scoring Model ◽  
Psychological Constructs ◽  
Credit Default ◽  
Credit History ◽  
The Impact ◽  
Credit Scoring Model

PurposeThe purpose of the study is to identify certain behavioural and psychological traits of the borrowers which have the tendency to predict the credit risk of the borrowers. And the second objective is to draw a conceptual model that reveals the impact of those traits on credit default.Design/methodology/approachThe study has adopted a systematic Literature Review approach to identify those behavioural and psychological traits of borrowers that reflect on the tendency to predict the credit default of borrowers.FindingsThe findings of this study have revealed that there are some non-financial factors, which can be looked into while granting a loan to a borrower. The identified factors can be used to develop a subjective credit scoring model that can quantify and verify the soft information (character and reliability) of debtors. Further, a behavioural credit scoring model will help in easing the assessment of those borrowers, who do not have an appropriate credit history and reliable financial statements.Practical implicationsThe proposed model would help banks and financial institutions to evaluate those borrowers who lack substantial financial information. Further, a subjective credit scoring model would help to evaluate the credit worthiness of such borrowers who do not have any credit history. The model would also reduce the biasness of subjective scoring and would reduce the financial constraints of borrowers.Originality/valueBy reviewing the literature, it has been observed that there are very few studies that have exclusively considered the behavioural and psychological factors in credit scoring. Several studies have linked the psychological constructs with debts, but very few researchers have considered it while constructing a behavioural scoring model. Thus, it can be inferred that this area of behavioural finance is still unexplored and needs attention of researchers worldwide. In addition, most of the studies are carried out in European, African and American regions but are almost non-existent in the Asian markets.

Credit scoring of small and medium enterprises: a behavioural approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Akanksha Goel ◽  
Shailesh Rastogi
Keyword(s):  
Small And Medium Enterprises ◽  
Credit Scoring ◽  
Reliability And Validity ◽  
Uttar Pradesh ◽  
Indian States ◽  
Psychological Traits ◽  
Content Type ◽  
Psychological Constructs ◽  
Credit Default ◽  
Medium Enterprises

Purpose This study aims to formulate a behavioural credit scoring models for Indian small and medium enterprises (SME) entrepreneurs using certain behavioural and psychological constructs. Two separate models are built which can predict the credit default and wilful default of the borrowers, respectively. This research was undertaken to understand whether certain psychological and behavioural factors can significantly predict the borrowers’ credit and wilful default. Design/methodology/approach A questionnaire survey was undertaken by SME entrepreneurs of two Indian states, i.e. Uttar Pradesh and Maharashtra. The questionnaire had two dependent variables: wilful default and credit default and nine independent variables. The questionnaire reliability and validity were ensured through confirmatory factor analysis (CFA) and further a model was built using logistic regression. Findings The results of this study have shown that certain behavioural and psychological traits of the borrowers can significantly predict borrowers’ default. These variables can be used to predict the overall creditworthiness of SME borrowers. Practical implications The findings of this research indicate that using behavioural and psychological constructs, lending institutions can easily evaluate the credit worthiness of those borrowers, who do not have any financial and credit history. This will enhance the capability of financial institutions to evaluate opaque SME borrowers. Originality/value There are very few numbers of studies which have considered predicting the credit default using certain psychological variables, but with respect to Asian market, and especially India, there does not exist a single significant study which has tried to fulfil such research gap. Also, this is the first study that has explored whether certain psychological factors can predict the wilful default of the borrowers. This is one of the most significant contributions of this research.

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.

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.

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.

Information asymmetry and equity issue decision of low-leverage firms

2018 ◽  
Vol 44 (11) ◽  
pp. 1330-1346
Author(s):  
Bipin Sony ◽  
Saumitra Bhaduri
Keyword(s):  
Information Asymmetry ◽  
Equity Issue ◽  
Content Type ◽  
Equity Issues ◽  
Developed Economies ◽  
Tangible Assets ◽  
Credit History ◽  
The Impact ◽  
Role Of Information

Purpose The purpose of this paper is to investigate the role of information asymmetry in the equity issue decision of two categories of Indian firms with distinct levels of information asymmetry – levered firms and unlevered firms. Design/methodology/approach This paper proposes a novel empirical approach to compare these two categories of firms. Levered firms exposed to the debt markets are under the scrutiny of lenders, reducing their information asymmetry problems. On the other hand, unlevered firms, which are smaller firms with fewer tangible assets and no credit history suffers more information problems. The authors use a propensity score matching method to identify firms that share similar firm-specific characteristics in these groups and compare equity issues to analyze the impact of information asymmetry. Findings The results show that information asymmetry plays a key role in the equity issue decision of Indian firms. Additionally, the authors find that the trends and characteristics of low-leverage (LL) firms in India are comparable to the LL from developed economies, which is consistent with the findings that they face more information problems. Originality/value Unlike the conventional approach of using proxy variables to capture information asymmetry, this study uses a novel framework where the authors compare the equity issue decision of similar firms in two categories with different degrees of information asymmetry.

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.

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