###### Abstract
Apart from these datasets, data obtained from peer-to-peer borrowing or social media platforms are also used [13; 14]. In addition, unlike the frequently used datasets based on credit history, risk assessment methods utilizing alternative data--such as psychometric, demographic, and e-mail information--also achieve successful results [15; 16]. For these reasons, it seems that using a new or alternative dataset is very important in the literature. As success rates in studies on credit risk assessment (CRA) have increased, so too has the complexity of the models used. In addition to making a maximum accuracy risk estimation, there is a growing need for these models to be explainable, transparent, and interpretable [17]. For example, in the United States, the Equal Credit Opportunity Act (ECOA) is intended to protect creditors from the risk of using \"black box\" credit scoring models by requiring them to provide specific reasons for applicants taking adverse action. The General Data Protection Regulation (GDPR) of the European Union also contains similar provisions. Financial institutions must consider interpretability while making financial choices according to the European Banking Authority (EBA) [18]. [PERSON] et al. aimed to enhance the interpretability of the decision-making process in credit card default prediction by introducing a new method for predicting credit card default that employs deep learning and Explainable Artificial Intelligence (XAI) techniques [19]. Similarly, [PERSON] et al. used the SHAP technique, a machine learning and XAI method, for predicting the credit risk of companies [20]. Thus, they rendered black box machine learning methods more explainable. The importance of machine learning and XAI methods in the field of CRA for the financial sector is increasing day by day, as shown by several studies [21]. Class imbalance is a significant problem for CRA. As seen in the dataset used in this study, people with low credit scores in particular constitute only a small portion of all customers. Although various studies have been conducted on machine learning methods for credit scoring, research on credit scoring in imbalanced datasets is not sufficient. Successful results have been obtained in studies conducted using SMOTE, one of the frequently used resampling methods for the problem of class imbalance in credit scoring [22; 23; 24]. When examining CRA approaches, it becomes evident that there is a need for a risk assessment method applicable to individuals without a credit history. The methods to be developed to address this need can contribute to the existing methods when used alongside traditional methods. In this study, we present a hybrid CRA method proposal based on the bill payment habits of individuals. Thus, this model can serve as a new model for those who cannot be assessed due to a lack of credit history, and as an alternative model for those who have a credit history and a score calculated using traditional methods. In the literature, there is a lack of up-to-date and real data usage in CRA studies conducted using machine learning methods [25]. In addition, it is quite difficult to access datasets containing real credit information [26; 27]. Most of the studies in the literature consist of two classes. Therefore, studies with multi-class real datasets are very important [28]. In our study, we created an alternative dataset by combining real bill payment habits with real CS calculated using traditional methods. The remainder of the paper is organized as follows: Section 2 includes the literature review; Section 3 includes materials and methods; Section 4 presents experimental results; Section 5 contains the conclusion. Finally, Section 6 concludes our article and indicates topics that can be a reference for future studies.