An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

Abstract

Financial threats are displaying a trend about the credit risk of commercial banks as the incredible improvement in the financial industry has arisen. In this way, one of the biggest threats faces by commercial banks is the risk prediction of credit clients. Recent studies mostly focus on enhancing the classifier performance for credit card default prediction rather than an interpretable model. In classification problems, an imbalanced dataset is also crucial to improve the performance of the model because most of the cases lied in one class, and only a few examples are in other categories. Traditional statistical approaches are not suitable to deal with imbalanced data. In this study, a model is developed for credit default prediction by employing various credit-related datasets. There is often a significant difference between the minimum and maximum values in different features, so Min-Max normalization is used to scale the features within one range. Data level resampling techniques are employed to overcome the problem of the data imbalance. Various undersampling and oversampling methods are used to resolve the issue of class imbalance. Different machine learning models are also employed to obtain efficient results. We developed the hypothesis of whether developed models using different machine learning techniques are significantly the same or different and whether resampling techniques significantly improves the performance of the proposed models. Oneway Analysis of Variance is a hypothesis-testing technique, used to test the significance of the results. The split method is utilized to validate the results in which data has split into training and test sets. The results on imbalanced datasets show the accuracy of 66.9% on Taiwan clients credit dataset, 70.7% on South German clients credit dataset, and 65% on Belgium clients credit dataset. Conversely, the results using our proposed methods significantly improve the accuracy of 89% on Taiwan clients credit dataset, 84.6% on South German clients credit dataset, and 87.1% on Belgium clients credit dataset. The results show that the performance of classifiers is better on the balanced dataset as compared to the imbalanced dataset. It is also observed that the performance of data oversampling techniques are better than undersampling techniques. Overall, the Gradient Boosted Decision Tree method performs better than other traditional machine learning classifiers. The Gradient Boosted Decision Tree method gives the best results while utilizing the K-means SMOTE oversampling method. Using one-way ANOVA, the null hypothesis was rejected by a p-value <0.001, hence confirming that the proposed model improved performance is statistical significance. The interpretable model is also deployed on the web to ease the different stakeholders. This model will help commercial banks, financial organizations, loan institutes, and other decision-makers to predict the loan defaulter earlier.

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

“…The AUC perfo rms b est when the dataset is imbalanced [100,101]. Our s t u dy h ad 16 imbalance datasets, so various studies [57,102,103] employed the AUC curve as a performance evaluation measure.…”

“…It resamples each subset of the data before using each integrated estimator. Therefore, its advantage over s cikit-learn is that it uses two additional paramet ers t hat control the behaviour of the random sampler: samplin g strategy and replace [57]. 2) Balanced Random Forest: This method first draws bootstrap samples from the minority class, then randomly draws with replacement the same numb er o f instances from the majority class, creating a b alan ced sample from which each tree is drawn.…”

“…But since they mainly use AdaBoost to train each bag, it is classified as ensemble methods [9,28]. Various ensemble-based resampling techniques , i.e., Balanced Bagging [57], Balanced Random Forest [58,61], Easy Ensemble [59], RUSBoost [60], and Balance Cascade [59], are widely known. Random balance, SMOTEBoost, and RUS-Boost are identical due to random balan ce.…”

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

“…However, with the help of modern techniques, we found that temperature and time are important features, and such findings may be used by other injection molding companies. Contributions of the research are similar to the prior studies that apply modern statistical methods in practical businesses [8].…”

A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models

“…The present study focused on the importance of other highly influential factors along with student academic results, such as the students per teacher ratio, the number of schools in a region, whether schools were located in rural or urban areas, the availability or lack of classrooms, electrical facilities in schools, availability or lack of furniture for students, open-air classes, computer lab facilities, science labs, and playgrounds in schools. Previous research [44][45][46][47][48] suggested that data pre-processing (normalisation, discretisation) techniques enhanced classifier performance, as these techniques reduce the biases among features. Furthermore, related studies showed that the min-max normalisation method performed better than other data normalisation methods [49][50][51].…”