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Commercial banks are required to explain the credit evaluation results to their customers. Therefore, banks attempt to improve the performance of their credit scoring models while ensuring the interpretability of the results. However, there is a tradeoff between the logistic regression model and machine learning-based techniques regarding interpretability and model performance because machine learning-based models are a black box. To deal with the tradeoff, in this study, we present a two-stage logistic regression method based on the Bayesian approach. In the first stage, we generate the derivative variables by linearly combining the original features with their explanatory powers based on the Bayesian inference. The second stage involves developing a credit scoring model through logistic regression using these derivative variables. Through this process, the explanatory power of a large number of original features can be utilized for default prediction, and the use of logistic regression maintains the model's interpretability. In the empirical analysis, the independent sample t-test reveals that our proposed approach significantly improves the model’s performance compared to that based on the conventional single-stage approach, i.e., the baseline model. The Kolmogorov–Smirnov statistics show a 3.42 percentage points (%p) increase, and the area under the receiver operating characteristic shows a 2.61%p increase. Given that our two-stage modeling approach has the advantages of interpretability and enhanced performance of the credit scoring model, our proposed method is essential for those in charge of banking who must explain credit evaluation results and find ways to improve the performance of credit scoring models.

Small- 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.

Credit scoring is one of the most successful applications of quantitative analysis in business. This paper shows how using survival-analysis tools from reliability and maintenance modeling allows one to build credit-scoring models that assess aspects of profit as well as default. This survival-analysis approach is also finding favor in credit-risk modeling of bond prices. The paper looks at three extensions of Cox's proportional hazards model applied to personal loan data. A new way of coarse-classifying of characteristics using survival-analysis methods is proposed. Also, a number of diagnostic methods to check adequacy of the model fit are tested for suitability with loan data. Finally, including time-by-characteristic interactions is proposed as a way of possible improvement of the model's predictive power.

The credit scoring model is one of the most important decision-making tools for the sustainability of banking systems. This study is the first to examine whether it can be improved by using system log data that are stoed extensively for system operation. We used the log data recorded by the mobile application system of KakaoBank, a leading internet bank used by more than 14 million people in Korea. After generating candidate variables from KakaoBank’s log data, we created a credit scoring model by utilizing variables with high information values and logistic regression, the most common method for developing credit scoring models in financial institutions. To prove our hypothesis on the improvement of credit scoring model performance, we performed an independent sample t-test using the simulation results of repeated model development and performance measurement based on randomly sampled data. Consequently, the discrimination power of the proposed model using logistic regression (neural network) compared to the credit bureau-based model significantly improved by 1.84 (2.22) percentage points based on the Kolmogorov–Smirnov statistics. The results of this study suggest that a bank can utilize the accumulated log data inside the bank to improve decision-making systems, including credit scoring, at a low cost.

Various types of government credit guarantee programs exist for small- and medium-sized enterprises (SMEs). The SMEs guaranteed by these programs can resolve their financial difficulties by obtaining loans from banks or being included in a pool for the issuance of primary collateralized bond obligations. However, the loan default rate for these supported firms is high owing to their moral hazard, which can be associated with unethical behavior in the accounting process. Since the stakeholders of credit guarantee programs initiated by the government include not only lenders and borrowers, but also taxpayers, the default risk of moral hazard must be minimized. Thus, an additional evaluation step is required to deal with accounting ethics, which has not thus far been considered in the literature. In this study, we propose an accounting ethics-based credit scoring model as a complementary approach, which can be used to select suitable borrowers. The proposed model is expected to reduce the default rate resulting from the moral hazard associated with unethical accounting behaviors in the supported firms.

The failure of banks to correctly analyze the credit worthiness of their customers has devastating consequences. Therefore, the importance of credit scoring in the banking sector has become a major field of research in recent years. There are some methods such as logistic regression, linear regression, discriminant analysis and artificial neural networks for credit scoring. The subject of this research is to evaluate the performance of machine learning and logistic regression models on credit scoring by comparison. In this study, it is aimed to develop a scorecard model in which banks can be exposed to a minimum level of credit risk by comparing the logistic regression and artificial neural network methods which are two of these methods. Although there are studies on the comparison of credit scoring models in the literature, the studies have been conducted through retail portfolios and a sample that covers a maximum of 4 years. Unlike the studies in the literature, this research was conducted through corporate firms and a larger sample than the studies in the literature. The result of the study indicated that artificial neural networks which have higher success than logistic regression on the development sample, saw lower success on the out of sample data. Thus, while artificial neural networks show higher performance, it is concluded that logistic regression provides more consistent results, and it is thought that artificial neural networks can produce more consistent results by optimization of the iteration processes.

A credit scoring model for technology-based small and medium enterprises presupposes evaluator objectivity and evaluation consistency; however, there is always some amount of error in any technology evaluation. This can be due in part to the subjective evaluation attributes that comprise part of the credit scoring model. The evaluated values of subjective attributes can vary among evaluators. In this study, we identified the significant characteristics of both evaluator and evaluation teams in terms of evaluation error using a decision tree analysis. Our results can improve the accuracy of a wide range of evaluation procedures for technology financing.

Recent finance and debt crises have made credit risk management one of the most important issues in financial research. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. In this paper, a novel feature-weighted support vector machine (SVM) credit scoring model is presented for credit risk assessment, in which an F-score is adopted for feature importance ranking. Considering the mutual interaction among modeling features, random forest is further introduced for relative feature importance measurement. These two feature-weighted versions of SVM are tested against the traditional SVM on two real-world datasets and the research results reveal the validity of the proposed method.

Purpose - Achieving equal treatment of credit applicants has been a legitimate concern of legislators and the credit industry. However, measures taken to date in attempting to comply with anti-discrimination laws arguably do not allow for the most effective use of credit scoring models, and could run counter-intuitive to the intention of legislation through indirect discrimination. The purpose of this paper is to offer an alternative interpretation that preserves the intention of legislation and also retains the integrity and effectiveness of credit scoring models.Design methodology approach - The paper makes a legal analysis of anti-discrimination laws in the UK, with US law as a comparison, aiming to demonstrate that concerns in using information protected under anti-discrimination laws as variables may be misplaced, because nothing in these laws precludes the inclusion of all relevant variables in modelling.Findings - The inclusion of variables representing protected characteristics in credit scoring models may not contradict current anti-discrimination laws.Research limitations implications - Limitations exist from the perspectives of customer relationship and the need for further checks and balances. Conclusive validation of the findings will need to come from the courts. The paper provides a springboard for empirical research on whether the inclusion of variables representing protected characteristics in credit scorecards continues to produce better decision-making models.Practical implications - The findings benefit credit risk modelling as a whole in facilitating the development of credit scorecards that are in compliance with anti-discrimination laws, without sacrificing their effectiveness.Originality value - The paper presents a fresh perspective and alternative solution to legal concerns regarding the use of protected characteristics in credit scoring, which will be useful to the credit industry.