Predicting creditworthiness in retail banking with limited scoring data

The preoccupation with modelling credit scoring systems including their relevance to predicting and decision making in the financial sector has been with developed countries, whilst developing countries have been largely neglected. The focus of our investigation is on the Cameroonian banking sector with implications for fellow members of the Banque des Etats de L'Afrique Centrale (BEAC) family which apply the same system. We apply logistic regression (LR), Classification and Regression Tree (CART) and Cascade Correlation Neural Network (CCNN) in building our knowledge-based scoring models. To compare various models’ performances, we use ROC curves and Gini coefficients as evaluation criteria and the Kolmogorov-Smirnov curve as a robustness test. The results demonstrate that an improvement in terms of predicting power from 15.69% default cases under the current system, to 7.68% based on the best scoring model, namely CCNN can be achieved. The predictive capabilities of all models are rated as at least very good using the Gini coefficient; and rated excellent using the ROC curve for CCNN. Our robustness test confirmed these results. It should be emphasised that in terms of prediction rate, CCNN is superior to the other techniques investigated in this paper. Also, a sensitivity analysis of the variables identifies previous occupation, borrower's account functioning, guarantees, other loans and monthly expenses as key variables in the forecasting and decision making processes which are at the heart of overall credit policy

Forecasting creditworthiness in retail banking: a comparison of cascade correlation neural networks, CART and logistic regression scoring models

The preoccupation with modelling credit scoring systems including their relevance to forecasting and decision making in the financial sector has been with developed countries whilst developing countries have been largely neglected. The focus of our investigation is the Cameroonian commercial banking sector with implications for fellow members of the Banque des Etats de L’Afrique Centrale (BEAC) family which apply the same system. We investigate their currently used approaches to assessing personal loans and we construct appropriate scoring models. Three statistical modelling scoring techniques are applied, namely Logistic Regression (LR), Classification and Regression Tree (CART) and Cascade Correlation Neural Network (CCNN). To compare various scoring models’ performances we use Average Correct Classification (ACC) rates, error rates, ROC curve and GINI coefficient as evaluation criteria. The results demonstrate that a reduction in terms of forecasting power from 15.69% default cases under the current system, to 3.34% based on the best scoring model, namely CART can be achieved. The predictive capabilities of all three models are rated as at least very good using GINI coefficient; and rated excellent using the ROC curve for both CART and CCNN. It should be emphasised that in terms of prediction rate, CCNN is superior to the other techniques investigated in this paper. Also, a sensitivity analysis of the variables identifies borrower’s account functioning, previous occupation, guarantees, car ownership, and loan purpose as key variables in the forecasting and decision making process which are at the heart of overall credit policy

Would credit scoring work for Islamic finance? A neural network approach

Purpose – The main aim of this paper is to distinguish whether the decision making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit; and highlight significant variables that are crucial in terms of accepting and rejecting applicants which can further aid the decision making process. Design/methodology/approach – A real data-set of 487 applicants are used consisting of 336 accepted credit applications and 151 rejected credit applications make to an Islamic finance house in the UK. In order to build the proposed scoring models, the data-set is divided into training and hold-out sub-set. The training sub-set is used to build the scoring models and the hold-out sub-set is used to test the predictive capabilities of the scoring models.70 percent of the overall applicants will be used for the training sub-set and 30 percent will be used for the testing sub-set. Three statistical modeling techniques namely Discriminant Analysis (DA), Logistic Regression (LR) and Multi-layer Perceptron (MP) neural network are used to build the proposed scoring models. Findings – Our findings reveal that the LR model has the highest Correct Classification (CC) rate in the training sub-set whereas MP outperforms other techniques and has the highest CC rate in the hold-out sub-set. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest Misclassification Cost (MC) above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision making process. Research limitations/implications – Although our sample is small and restricted to an Islamic Finance house in the UK the results are robust. Future research could consider enlarging the sample in the UK and also internationally allowing for cultural differences to be identified. The results indicate that the scoring models can be of great benefit to Islamic finance houses in regards to their decision making processes of accepting and rejecting new credit applications and thus improve their efficiency and effectiveness. Originality/value –Our contribution is the first to apply credit scoring modeling techniques in Islamic Finance. Also in building a scoring model our application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected

An Analysis of Factors Influencing Customer Creditworthiness in the Banking Sector of Kingdom of Bahrain

This research is based on Bahraini bankers’ perception on the factors influencing customer creditworthiness in the banking sector of Kingdom of Bahrain. We consider that the research was done in the Kingdom of Bahrain which has a growing banking industry. To enhance the whole procedure of the creditworthiness, it is vital for an employer to understand the most important factors influencing customer creditworthiness. The purpose of the study was to investigate the factors influencing customers creditworthiness in the banking industry. The creditworthiness can be assessed through qualitative factors, quantitative factors and risk factors. The research was conducted through a survey, using the questionnaire as the research instrument. The respondents of the study are employees of banks across the Kingdom dealing with creditworthiness. The statistical tools used in the study are Multiple Regression Analyses and weighted mean. The researcher has found that there is significant relationship between all three factors and creditworthiness, and they don’t equally influence the creditworthiness. The research provides recommendations to banks in assessing the creditworthiness. The researcher recommended that employees must use the most effective methods such as credit scoring to conduct the analysis of creditworthiness in order to make effective decisions. Moreover, the researcher recommended that analysts should take into considerations the most effective factors in the analysis process and they must not neglect other

Credit bureaus between risk-management, creditworthiness assessment and prudential supervision

"This text may be downloaded for personal research purposes only. Any additional reproduction for other purposes, whether in hard copy or electronically, requires the consent of the author. If cited or quoted, reference should be made to the full name of the author, the title, the working paper or other series, the year, and the publisher."This paper discusses the role and operations of consumer Credit Bureaus in the European Union in the context of the economic theories, policies and law within which they work. Across Europe there is no common practice of sharing the credit data of consumers which can be used for several purposes. Mostly, they are used by the lending industry as a practice of creditworthiness assessment or as a risk-management tool to underwrite borrowing decisions or price risk. However, the type, breath, and depth of information differ greatly from country to country. In some Member States, consumer data are part of a broader information centralisation system for the prudential supervision of banks and the financial system as a whole. Despite EU rules on credit to consumers for the creation of the internal market, the underlying consumer data infrastructure remains fragmented at national level, failing to achieve univocal, common, or defined policy objectives under a harmonised legal framework. Likewise, the establishment of the Banking Union and the prudential supervision of the Euro area demand standardisation and convergence of the data used to measure debt levels, arrears, and delinquencies. The many functions and usages of credit data suggest that the policy goals to be achieved should inform the legal and institutional framework of Credit Bureaus, as well as the design and use of the databases. This is also because fundamental rights and consumer protection concerns arise from the sharing of credit data and their expanding use

FinTech, RegTech and the role of alternative lending: an analysis of the P2P platform LendingClub

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Islamic Personality Model as Psychometric Tool To Assess Creditworthiness of Micro Financing

This study aims to develop an Islamic personality model as a psychometric tool to assess creditworthiness as an alternative predictive character analysis for micro businesses. The method designed to formulate the proposed model coded in R Studio uses two approaches. First, we modify Moslem Religiosity Personality Inventory and then frame a structural model based on Partial Least Square. Subsequently, we use the random forest technique to see the model's accuracy. The result shows a valid and reliable model and performs with 89.47 % accuracy with an Area Under Curve -Receiver Operating Characteristic of 90.06 %. This model implies a solution to strengthen the assessment of the character of creditworthiness of a potential micro-business and helps Islamic Financial Institutions to assess prospective micro-business to determine credit risk and pricing.JEL Classification: B41, D81, D87, G21, P43How to Cite:Hardiansyah., Amalia, E., & Hamid, A. (2023). Islamic Personality Model as Pychometric Tool To Access Creditworthiness of Micro Financing. Etikonomi, 22(1), 233–246. https://doi.org/10.15408/etk.v22i2.30370

Credit-Scoring Methods (in English)

The paper reviews the best-developed and most frequently applied methods of credit scoring employed by commercial banks when evaluating loan applications. The authors concentrate on retail loans – applied research in this segment is limited, though there has been a sharp increase in the volume of loans to retail clients in recent years. Logit analysis is identified as the most frequent credit-scoring method used by banks. However, other nonparametric methods are widespread in terms of pattern recognition. The methods reviewed have potential for application in post-transition countries.banking sector, credit scoring, discrimination analysis, pattern recognition, retail loans

USING NON-FINANCIAL DATA TO ASSESS THE CREDITWORTHINESS OF BUSINESSES IN ONLINE TRADE

Assessing the creditworthiness of prospective business partners is the first step in conducting trade. Traditionally the creditworthiness of partners was assessed using transactional methods, methods based on close observation of the other party and the heavy use of mostly subjective, soft information. During recent decades, however, these relational methods were largely replaced with transactional methods relying almost exclusively on objective financial data, otherwise known as hard data. A considerable portion of firms involved in business to business trade are now small companies that do not to have any reliable or comparable financial information. In the absence of such information, transactional methods of assessing business creditworthiness have very limited practical value. To provide a remedy, this study proposes using non-financial information available from the Web and thus a return to the more transactional methods. We identify sources of credit-related information available from a typical business to business exchange and test if such information can predict firm creditworthiness. We conduct a study with a group of online businesses on a major B2B exchange and empirically show that a number of non-financial factors can in fact predict online businesses’ level of creditworthiness