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The credit ratings issued by the Big 3 ratings agencies are inaccurate and slow to respond to market changes. This paper aims to develop a rigorous, transparent and robust credit assessment and rating scheme for sovereigns.

This paper develops a regression-based model using credit default swap (CDS) data, and data on financial and macroeconomic variables to estimate sovereign CDS spreads. Using these spreads, the default probabilities of sovereigns can be estimated. The new ratings scheme is then used in conjunction with these default probabilities to assign credit ratings to sovereigns.

The developed model accurately estimates CDS spreads (based on RMSE values). Credit ratings issued retrospectively using the new scheme reflect reality better.

This paper reveals that both macroeconomic and financial factors affect both systemic and idiosyncratic risks for sovereigns.

The developed credit assessment and ratings scheme can be used to evaluate the creditworthiness of sovereigns and subsequently assign robust credit ratings.

The transparency and rigor of the new scheme will result in better and trustworthy indications of a sovereign’s financial health. Investors and monetary authorities can make better informed decisions. The episodes that occurred during the debt crisis could be avoided.

This paper uses both financial and macroeconomic data to estimate CDS spreads and demonstrates that both financial and macroeconomic factors affect sovereign systemic and idiosyncratic risk. The proposed credit assessment and ratings schemes could supplement or potentially replace the credit ratings issued by the Big 3 ratings agencies.

Current models of transaction credit in the e-commerce network face many problems, such as the one-sided measurement, low accuracy and insufficient anti-aggression solutions. This paper aims to address these problems by studying the transaction credit problem in the crowd transaction network.

This study divides the transaction credit into two parts, direct transaction credit and recommended transaction credit, and it proposes a model based on the crowd transaction network. The direct transaction credit comprehensively includes various factors influencing the transaction credit, including transaction evaluation, transaction time, transaction status, transaction amount and transaction times. The recommendation transaction credit introduces two types of recommendation nodes and constructs the recommendation credibility for each type. This paper also proposes a “buyer + circle of friends” method to store and update the transaction credit data.

The simulation results show that this model is superior with high accuracy and anti-aggression.

The direct transaction credit improves the accuracy of the transaction credit data. The recommendation transaction credit strengthens the anti-aggression of the transaction credit data. In addition, the “buyer + circle of friends” method fully uses the computing of the storage ability of the internet, and it also solves the failure problem of using a single node.

Although credit unions are nonprofit organizations, their objectives depend on the efficient management of their resources and credit risk aligned with the principles of the cooperative doctrine. This paper aims to propose the combined use of credit scoring and profit scoring to increase the effectiveness of the loan-granting process in credit unions.

This sample is composed by the data of personal loans transactions of a Brazilian credit union.

The analysis reveals that the use of statistical methods improves significantly the predictability of default when compared to the use of subjective techniques and the superiority of the random forests model in estimating credit scoring and profit scoring when compared to logit and ordinary least squares method (OLS) regression. The study also illustrates how both analyses can be used jointly for more effective decision-making.

Replacing subjective analysis with objective credit analysis using deterministic models will benefit Brazilian credit unions. The credit decision will be based on the input variables and on clear criteria, turning the decision-making process impartial. The joint use of credit scoring and profit scoring allows granting credit for the clients with the highest potential to pay debt obligation and, at the same time, to certify that the transaction profitability meets the goals of the organization: to be sustainable and to provide loans and investment opportunities at attractive rates to members.

Loan default risk or credit risk evaluation is important to financial institutions which provide loans to businesses and individuals. Loans carry the risk of being defaulted. To understand the risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amounts of information on borrowers. Statistical predictive analytic techniques can be used to analyse or to determine the risk levels involved in loans. This paper aims to address the question of default prediction of short-term loans for a Tunisian commercial bank.

The authors have used a database of 924 files of credits granted to industrial Tunisian companies by a commercial bank in the years 2003, 2004, 2005 and 2006. The naive Bayesian classifier algorithm was used, and the results show that the good classification rate is of the order of 63.85 per cent. The default probability is explained by the variables measuring working capital, leverage, solvency, profitability and cash flow indicators.

The results of the validation test show that the good classification rate is of the order of 58.66 per cent; nevertheless, the error types I and II remain relatively high at 42.42 and 40.47 per cent, respectively. A receiver operating characteristic curve is plotted to evaluate the performance of the model. The result shows that the area under the curve criterion is of the order of 69 per cent.

The paper highlights the fact that the Tunisian central bank obliged all commercial banks to conduct a survey study to collect qualitative data for better credit notation of the borrowers.

El riesgo de incumplimiento de préstamos o la evaluación del riesgo de crédito es importante para las instituciones financieras que otorgan préstamos a empresas e individuos. Existe el riesgo de que el pago de préstamos no se cumpla. Para entender los niveles de riesgo de los usuarios de crédito (corporaciones e individuos), los proveedores de crédito (banqueros) normalmente recogen gran cantidad de información sobre los prestatarios. Las técnicas analíticas predictivas estadísticas pueden utilizarse para analizar o determinar los niveles de riesgo involucrados en los préstamos. En este artículo abordamos la cuestión de la predicción por defecto de los préstamos a corto plazo para un banco comercial tunecino.

Utilizamos una base de datos de 924 archivos de créditos concedidos a empresas industriales tunecinas por un banco comercial en 2003, 2004, 2005 y 2006. El algoritmo bayesiano de clasificadores se llevó a cabo y los resultados muestran que la tasa de clasificación buena es del orden del 63.85%. La probabilidad de incumplimiento se explica por las variables que miden el capital de trabajo, el apalancamiento, la solvencia, la rentabilidad y los indicadores de flujo de efectivo.

Los resultados de la prueba de validación muestran que la buena tasa de clasificación es del orden de 58.66% ; sin embargo, los errores tipo I y II permanecen relativamente altos, siendo de 42.42% y 40.47%, respectivamente. Se traza una curva ROC para evaluar el rendimiento del modelo. El resultado muestra que el criterio de área bajo curva (AUC, por sus siglas en inglés) es del orden del 69%.

El documento destaca el hecho de que el Banco Central tunecino obligó a todas las entidades del sector llevar a cabo un estudio de encuesta para recopilar datos cualitativos para un mejor registro de crédito de los prestatarios.

Curva ROC, Evaluación de riesgos, Riesgo de incumplimiento, Sector bancario, Algoritmo clasificador bayesiano.

Artículo de investigación

This paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs).

The research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation was carried out using the Scopus, ScienceDirect and Web of Science databases. The papers selection and classification took place in three steps considering only studies in English language and published in electronic journals (from 2008 to 2022). The investigation led up to the selection of 46 publications (10 presenting literature reviews and 36 proposing CSMs).

The findings showed that CSMs are usually formulated using Financial Analysis, Machine Learning, Statistical Techniques, Operational Research and Data Mining Algorithms. The main databases used by the researchers were banks and the University of California, Irvine. The analyses identified 48 methods used by CSMs, the main ones being: Logistic Regression (13%), Naive Bayes (10%) and Artificial Neural Networks (7%). The authors conclude that advances in credit score studies will require new hybrid approaches capable of integrating Big Data and Deep Learning algorithms into CSMs. These algorithms should have practical issues considered consider practical issues for improving the level of adaptation and performance demanded for the CSMs.

The results of this study might provide considerable practical implications for the application of CSMs. As it was aimed to demonstrate the application of optimisation methods, it is highly considerable that legal and ethical issues should be better adapted to CSMs. It is also suggested improvement of studies focused on micro and small companies for sales in instalment plans and commercial credit through the improvement or new CSMs.

The economic reality surrounding credit granting has made risk management a complex decision-making issue increasingly supported by CSMs. Therefore, this paper satisfies an important gap in the literature to present an analysis of recent advances in optimisation methods applied to CSMs. The main contribution of this paper consists of presenting the evolution of the state of the art and future trends in studies aimed at proposing better CSMs.

This study aims to examine and compare the credit risk management (CRM) scenario of Islamic banks (IBs) and conventional banks (CBs) in Pakistan, keeping in view the phenomenal growth of Islamic banking and its future implications.

A sample of five CBs and four IBs was chosen out of the whole banking industry for the study. Secondary data obtained from the banks’ annual financial reports for 13 years, starting from 2004 to 2016, were analyzed. Multiple regression, correlation and descriptive analysis were used in the examination of the data.

The results show that loan quality (LQ) has a positive and significant impact on CRM for both IBs and CBs. Asset quality (AQ), on the other hand, has a negative impact on CRM in the case of IBs, but has a significantly positive relation with CRM in the case of CBs. The impact of 16 ratios measuring LQ and AQ have also been individually checked on CRM, by making use of a regression model using a dummy variable of financial crises for robust comparison among CBs and IBs. The model proved significant, and CRM performance of IBs was observed to be better than that of CBs. Moreover, the mean average value of financial ratios used as a measuring tool for these variables shows that the CRM performance of IBs operating in Pakistan was better than that of CBs over the period of the study.

The research findings are expected to facilitate bankers, investors, academics and policy makers to build a better understanding of CRM practices as adopted by CBs and IBs. The findings would be useful in formulating policy measures for the progress of the banking industry in Pakistan.

This research is unique in terms of its approach toward analyzing and comparing CRM performance of CBs and IBs. Such work has not been carried out before in the Pakistani banking industry.

Determinants of credit growth in Saudi Arabia are investigated.

A panel approach is applied to macroeconomic and bank-level data spanning 2000 ‐15.

Bank lending is supported by strong bank balance sheet conditions (high capital ratio, and growth of NPL provisioning and deposits), and higher growth of both oil prices and non-oil private sector GDP. Lower bank concentration also helps, likely through greater competition, so does stronger institution. Consistent with the literature, lending by Islamic banks may be more responsive to economic activity. Lending remained robust in 2015 despite oil prices having declined, helped by strong bank balance sheets and as banks reduced their holdings of “excess liquidity”. To support bank lending in the period ahead, bank balance sheets need to remain strong. Fiscal adjustment and a reduced reliance on banks to finance the budget deficit would support credit provision to the private sector.

The paper is first to analyze in detail determinants of bank lending in Saudi Arabia applying a panel approach to bank level data, and draws critical policy implications.

This paper investigates whether and how the financial services offered by an e-commerce platform can help budget-constrained small- and medium-sized suppliers improve their corporate social responsibility (CSR) performance.

The authors examine a supply chain in which a budget-constrained supplier engages in CSR, produces a finished product and sells it on the marketplace of an e-commerce platform. This platform offers financial services to the supplier, choosing among three types of financing models: target rate of return, credit limit and CSR-linked financing. Using a Stackelberg game approach, the authors can drive the equilibrium decisions under each financing model.

The results reveal that all three financing models help improve the supplier's CSR investment as long as consumer sensitivity toward CSR exists. Moreover, the last one leads to the highest profitability of the overall supply chain and each member.

The findings shed light on the role of platform financing and how to design the most appropriate financing model to improve CSR for supply chain managers.

Personal bankruptcy is on the rise in Malaysia. The Insolvency Department of Malaysia reported that personal bankruptcy has increased since 2007, and the total accumulated personal bankruptcy cases stood at 131,282 in 2014. This is indeed an alarming issue because the increasing number of personal bankruptcy cases will have a negative impact on the Malaysian economy, as well as on the society. From the aspect of individual’s personal economy, bankruptcy minimizes their chances of securing a job. Apart from that, their account will be frozen, lost control on their assets and properties and not allowed to start any business nor be a part of any company’s management. Bankrupts also will be denied from any loan application, restricted from travelling overseas and cannot act as a guarantor. This paper aims to investigate this problem by developing the personal bankruptcy prediction model using the decision tree technique.

In this paper, bankrupt is defined as terminated members who failed to settle their loans. The sample comprised of 24,546 cases with 17 per cent settled cases and 83 per cent terminated cases. The data included a dependent variable, i.e. bankruptcy status (Y = 1(bankrupt), Y = 0 (non-bankrupt)) and 12 predictors. SAS Enterprise Miner 14.1 software was used to develop the decision tree model.

Upon completion, this study succeeds to come out with the profiles of bankrupts, reliable personal bankruptcy scoring model and significant variables of personal bankruptcy.

This decision tree model is possible for patent and income generation. Financial institutions are able to use this model for potential borrowers to predict their tendency toward personal bankruptcy.

Create awareness to society on significant variables of personal bankruptcy so that they can avoid being a bankrupt.

This decision tree model is able to facilitate and assist financial institutions in evaluating and assessing their potential borrower. It helps to identify potential defaulting borrowers. It also can assist financial institutions in implementing the right strategies to avoid defaulting borrowers.