Review of credit risk and credit scoring models based on computing paradigms in financial institutions

2021 ◽  
Author(s):  
Deepika Sharma ◽  
Ashutosh Vashistha ◽  
Manoj Gupta
Keyword(s):  
Credit Risk ◽  
Financial Institutions ◽  
Credit Scoring

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 Modelling the predictive performance of credit scoring

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Shi-Wei Shen ◽  
Tri-Dung Nguyen ◽  
Udechukwu Ojiako
Keyword(s):  
Credit Risk ◽  
Financial Institutions ◽  
Goodness Of Fit ◽  
Predictive Power ◽  
Risk Index ◽  
Credit Scoring ◽  
Explanatory Power ◽  
Predictive Performance ◽  
Stock Index ◽  
Macroeconomic Variables

Orientation: The article discussed the importance of rigour in credit risk assessment.Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan.Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities.Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems.Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI), micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk.Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product.Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

scholarly journals The Vicious Circle of Non-performing Assets: An introspection for Indian banks to ensure their profitability amidst COVID – 19 pandemic

2020 ◽  
pp. 179-185
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Financial Institutions ◽  
Service Providers ◽  
Credit Scoring ◽  
Developed Countries ◽  
Basic Function ◽  
Credit Risk Assessment ◽  
Indian Banks ◽  
Risk Assessment Mechanism

The non performing assets (NPAs) or bad loans, as we understand generally, have always been one of the key challenges for Indian banks and financial institutions and they have been adversely affecting the sustainability of these financial service providers. While performing the basic function of extending credit in order to earn interest income, however, it is also important for these institutions to have an efficient and effective credit risk assessment mechanism in place, so that, a proper balance between profitability and sustainability is maintained. Credit scoring models are one of the most important components of credit risk assessment mechanism and banks and financial institutions of many developed countries have developed credit scoring models based on advanced technologies. On the contrary, most of the Indian banks are still dependent on the traditional way of developing credit scoring models, which might be a deterrent against ensuring safe credit policy amidst the COVID – 19 pandemic.

Financial institutions mergers: a strategy choice of wealth maximisation and economic value

2020 ◽  
Vol 12 (4) ◽  
pp. 495-529
Author(s):  
Mohamad Hassan ◽  
Evangelos Giouvris
Keyword(s):  
Credit Risk ◽  
Event Study ◽  
Financial Institutions ◽  
Value Creation ◽  
Economic Value ◽  
Abnormal Returns ◽  
Study Analysis ◽  
Event Window ◽  
Content Type ◽  
Merger Announcement

Purpose This study Investigates Shareholders' value adjustment in response to financial institutions (FIs) merger announcements in the immediate event window and in the extended event window. This study also investigates accounting measures performance, comparison of post-merger to pre-merger, including several cash flow measures and not just profitability measures, as the empirical literature review suggests. Finally, the authors examine FIs mergers orientations of diversification and focus create more value for shareholders (in the immediate announcement window and several months afterward) and/or generates better cash flows, profitability and less credit risk. Design/methodology/approach This study examines FIs merger effect on bidders’ shareholder’s value and on their observed performance. This examination deploys three techniques simultaneously: a) an event study analysis, to estimate and calculate abnormal returns (ARs) and cumulative abnormal returns (CARs) in the narrow windows of the merger announcement, b) buy and hold event study analysis, to estimate ARs in the wider window of the event, +50 to +230 days after the merger announcement and c) an observed performance analysis, of financial and capital efficiency measures before and after the merger announcement; return on equity, liquidity, cost to income ratio, capital to total assets ratio, net loans to total loans, credit risk, loans to deposits ratio, other expenses and total assets, economic value addition, weighted average cost of capital and return on invested capital. Deal criteria of value, mega-deals, strategic orientation (as in Ansoff (1980) growth strategies), acquiring bank size and payment method are set as individually as control variables. Findings Results show that FIs mergers destroy share value for the bidding firms pursuing a market penetration strategy. Market development and product development strategies enable shareholders’ value creation in short and long horizons. Diversification strategies do not influence bidding shareholders’ value. Local bank to bank mergers create shareholders’ value and enhance liquidity and economic value in the short run. Bank to bank cross border mergers create value for bidders’ in the long term but are associated with high costs and higher risks. Originality/value A significant advancement over the current literature is in assessing mergers, not only for bank bidders but also for the three pillars FIs of the financial sector; banks, real-estate companies and investment companies mergers. It is an improvement over current finance literature because it deploys two different strategies in the analysis. At a univariate level, shareholder value creation and market reaction to merger announcements are examined over short (−5 or +5 days) and long (+230 days) windows of the event. Followed by regressing, the resultant CARs and BHARs over financial performance variables at the multivariate level.

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 The Implementation of Credit Risk Scorecard Using Ontology Design Patterns and BCBS 239

2020 ◽  
Vol 20 (2) ◽  
pp. 93-104
Author(s):  
Jalil Elhassouni ◽  
Abderrahim El qadi ◽  
Yasser El madani El alami ◽  
Mohamed El haziti
Keyword(s):  
Credit Risk ◽  
Information And Communication Technologies ◽  
Financial Institutions ◽  
Design Patterns ◽  
Decisive Role ◽  
Credit Risk Management ◽  
Technology Infrastructure ◽  
Ontology Design ◽  
Information And Communication ◽  
The 2008 Financial Crisis

AbstractNowadays information and communication technologies are playing a decisive role in helping the financial institutions to deal with the management of credit risk. There have been significant advances in scorecard model for credit risk management. Practitioners and policy makers have invested in implementing and exploring a variety of new models individually. Coordinating and sharing information groups, however, achieved less progress. One of several causes of the 2008 financial crisis was in data architecture and information technology infrastructure. To remedy this problem the Basel Committee on Banking Supervision (BCBS) outlined a set of principles called BCBS 239. Using Ontology Design Patterns (ODPs) and BCBS 239, credit risk scorecard and applicant ontologies are proposed to improve the decision making process in credit loan. Both ontologies were validated, distributed in Ontology Web Language (OWL) files and checked in the test cases using SPARQL. Thus, making their (re)usability and expandability easier in financial institutions. These ontologies will also make sharing data more effective and less costly.

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