Performance Of Credit Scoring Research Articles

On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance

For more than a half-century, credit risk management has used credit scoring models at each of the well-defined stages of credit risk management. Application scoring is used to decide whether to grant a loan or not, while behavioral scoring is used mainly for portfolio management and to take preventive actions in case of default signals. In both cases, social network data has recently been shown to be valuable to increase the predictive power of these models, especially when the borrower’s historical data is scarce or not available. This study aims to understand the dynamics of creditworthiness assessment performance and how it is influenced by credit history, repayment behavior, and social network features. To accomplish this, we build up a machine learning classification framework demonstrating its value analyzing 97,000 individuals and companies from the moment they obtained their first loan up to 12 months afterward. Our original and massive dataset allowed us to characterize each borrower according to its credit behavior, and socioeconomic relationships. Our study finds that credit scoring based on borrowers’ history improves performance at a decreasing rate during the first six months and then stabilizes. The most notable effect on the performance of credit scoring based on social network features occurs in loan applications; for personal scoring, this effect prevails for approximately six months, while for business scoring, social network features add value throughout the entire study period. These findings are of great value to improve credit risk management and optimize the combined use of both the traditionally exploited information and new alternative data sources.

Extreme Learning Machine Enhanced Gradient Boosting for Credit Scoring

Credit scoring is an effective tool for banks and lending companies to manage the potential credit risk of borrowers. Machine learning algorithms have made grand progress in automatic and accurate discrimination of good and bad borrowers. Notably, ensemble approaches are a group of powerful tools to enhance the performance of credit scoring. Random forest (RF) and Gradient Boosting Decision Tree (GBDT) have become the mainstream ensemble methods for precise credit scoring. RF is a Bagging-based ensemble that realizes accurate credit scoring enriches the diversity base learners by modifying the training object. However, the optimization pattern that works on invariant training targets may increase the statistical independence of base learners. GBDT is a boosting-based ensemble approach that reduces the credit scoring error by iteratively changing the training target while keeping the training features unchanged. This may harm the diversity of base learners. In this study, we incorporate the advantages of the Bagging ensemble training strategy and boosting ensemble optimization pattern to enhance the diversity of base learners. An extreme learning machine-based supervised augmented GBDT is proposed to enhance the discriminative ability for credit scoring. Experimental results on 4 public credit datasets show a significant improvement in credit scoring and suggest that the proposed method is a good solution to realize accurate credit scoring.

Can System Log Data Enhance the Performance of Credit Scoring?—Evidence from an Internet Bank in Korea

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.

An Analysis of Factors Affecting Credit Scoring Performance in SMEs

In small and medium enterprises (SMEs), both financial and non-financial indicators related to the company and the large shareholder have significant effects on credit scores. This study explores the hybrid nature of SME credit scoring systems by developing an SME credit scoring model with general indicators, financial indicators and intelligence indicators related to the company and the major shareholder. This study is the first to explore the effect of intelligence indicators in credit scoring. It used regression analysis to evaluate real data from 125 SMEs operating in Turkey. Of 17 independent variables, 9 had statistically significant relationships with SME credit scores. The paper includes a comprehensive discussion of the implications of these findings.

A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

AbstractCredit scoring focuses on the development of empirical models to support the financial decision‐making processes of financial institutions and credit industries. It makes use of applicants' historical data and statistical or machine learning techniques to assess the risk associated with an applicant. However, the historical data may consist of redundant and noisy features that affect the performance of credit scoring models. The main focus of this paper is to develop a hybrid model, combining feature selection and a multilayer ensemble classifier framework, to improve the predictive performance of credit scoring. The proposed hybrid credit scoring model is modeled in three phases. The initial phase constitutes preprocessing and assigns ranks and weights to classifiers. In the next phase, the ensemble feature selection approach is applied to the preprocessed dataset. Finally, in the last phase, the dataset with the selected features is used in a multilayer ensemble classifier framework. In addition, a classifier placement algorithm based on the Choquet integral value is designed, as the classifier placement affects the predictive performance of the ensemble framework. The proposed hybrid credit scoring model is validated on real‐world credit scoring datasets, namely, Australian, Japanese, German‐categorical, and German‐numerical datasets.

Effect Analysis of Resampling Techniques on the Performance of Customer Credit Scoring Models

In customer credit scoring, the class distributions of credit scoring datasets are usually imbalanced, which severely affects the performance of credit scoring models. To solve the problem, we introduced 4 evaluation criteria, used 9 classification methods, analyzed the performance of 5 commonly used resampling techniques by extensive experiments on 3 real credit scoring datasets, and performed nonparametric tests on the results. We can conclude that: resampling improves the performance of credit scoring, and the degrees of improvement depend on the evaluation criteria selected; generally, SMOTE performs best; the best resampling technique varies with different models and SMOTE works well with most models. Therefore, the application of resampling techniques should be based on evaluation criteria and models selected.

Credit Scoring and Loan Default

AbstractA metric of credit score performance is developed to study the usage and performance of credit scoring in the loan origination process. We examine the performance of origination FICO scores as measures of ex ante borrower creditworthiness using loan‐level data on ex post performance of subprime mortgages. Parametric and nonparametric estimates of credit score performance reveal different trends, especially on originations with low credit scores. The data suggest a trend of increased emphasis on higher credit scores accompanying a trend of increased riskiness in other origination attributes. Over time, this increased emphasis on credit scoring coincided with deterioration in FICO performance largely because of the fact that higher credit score originations of later cohorts were more likely to have riskier attributes. However, controlling for other attributes on originations and changes in economic conditions, we find that, as measures of borrower ranking, FICO performance on subprime loans over the years remains fairly stable.

Modelling the predictive performance of credit scoring

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.

The impact of sample bias on consumer credit scoring performance and profitability

This article seeks to gain insight into the influence of sample bias in a consumer credit scoring model. In earlier research, sample bias has been suggested to pose a sizeable threat to predictive performance and profitability due to its implications on either population drainage or biased estimates. Contrary to previous—mainly theoretical—research on sample bias, the unique features of the data set used in this study provide the opportunity to investigate the issue in an empirical setting. Based on the data of a mail-order company offering short-term consumer credit to their consumers, we show that (i) given a certain sample size, sample bias has a significant effect on consumer credit-scoring performance and profitability, (ii) its effect is composed of the inclusion of rejected orders in the scoring model, and—to a lesser extent—the inclusion of these orders into the variable-selection process, and (iii) the impact of the effect of sample bias on consumer credit-scoring performance and profitability is modest.

A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines

The objective of the proposed study is to explore the performance of credit scoring using a two-stage hybrid modeling procedure with artificial neural networks and multivariate adaptive regression splines (MARS). The rationale under the analyses is firstly to use MARS in building the credit scoring model, the obtained significant variables are then served as the input nodes of the neural networks model. To demonstrate the effectiveness and feasibility of the proposed modeling procedure, credit scoring tasks are performed on one bank housing loan dataset using cross-validation approach. As the results reveal, the proposed hybrid approach outperforms the results using discriminant analysis, logistic regression, artificial neural networks and MARS and hence provides an alternative in handling credit scoring tasks.

Mining the customer credit using classification and regression tree and multivariate adaptive regression splines

Credit scoring has become a very important task as the credit industry has been experiencing severe competition during the past few years. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the relative importance of potential input variables, long training process, and interpretative difficulties have often been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring using two commonly discussed data mining techniques—classification and regression tree (CART) and multivariate adaptive regression splines (MARS). To demonstrate the effectiveness of credit scoring using CART and MARS, credit scoring tasks are performed on one bank credit card data set. As the results reveal, CART and MARS outperform traditional discriminant analysis, logistic regression, neural networks, and support vector machine (SVM) approaches in terms of credit scoring accuracy and hence provide efficient alternatives in implementing credit scoring tasks.

Credit scoring using the hybrid neural discriminant technique

Abstract Credit scoring has become a very important task as the credit industry has been experiencing double-digit growth rate during the past few decades. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the decision of network's topology, importance of potential input variables and the long training process has often long been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring by integrating the backpropagation neural networks with traditional discriminant analysis approach. To demonstrate the inclusion of the credit scoring result from discriminant analysis would simplify the network structure and improve the credit scoring accuracy of the designed neural network model, credit scoring tasks are performed on one bank credit card data set. As the results reveal, the proposed hybrid approach converges much faster than the conventional neural networks model. Moreover, the credit scoring accuracies increase in terms of the proposed methodology and outperform traditional discriminant analysis and logistic regression approaches.