## 5 Conclusions  
The dataset analyzed in this study was generated from invoice payment data belonging to a payment institution in Turkey. Using invoice payment transactions, person-based payment habits were created and combined with real credit scores to obtain the final dataset that is the subject of this study. The dataset has a 10-class and unbalanced structure. First, the raw dataset was classified using various algorithms, including LR, DT, SVM, RF, EXT, NB, and MLP. Following this, preprocessing steps were applied to clean the data by removing missing values, outliers, and low-quality samples. The dataset was then normalized using min-max scaling and subjected to feature selection by means of the ANOVA F-test, chi-square, and mutual information methods. The dataset was balanced with the SMOTE method and the classification performances were re-evaluated. The highest performance was achieved in the model created with the ANOVA F-test feature selection, SMOTE oversampling, and EXT classification methods. This model was examined and evaluated in terms of explainability and interpretability with the LIME and SHAP XAI techniques. This study emphasizes the importance of the data preprocessing step. Missing values and outlier data in the dataset were identified and removed. In addition, data quality and consistency were assessed. Samples sourced from a relational database were analyzed to eliminate data lacking relational counterparts or containing incorrect relationships. This thorough cleaning process ensured more accurate training of the machine learning model and ultimately improved performance. One of the most important parameters of ML models is feature selection. With the feature selection methods used in this study, features that do not contribute to the model were removed from the dataset and the most efficient feature set was used. Given that the dataset has a multi-class and imbalanced structure, it was trained by balancing it using the artificial sampling method. The exploration and application of these artificial sampling techniques to address data imbalance, a common issue in real CRA datasets, will be insightful for future studies. In the tests conducted in this study, tree-based classification algorithms demonstrated high performance in CRA. Performance analyses were run using various evaluation metrics, including ACC, PRE, REC, F1 score, F2 score, and AUC to assess reliability. The best performance was achieved with the EXT algorithm. ACC was measured as 80.49% and AUC as 97.04%. In addition, PRE was 79.89, REC was 80.42, F1 score was 79.83%, and F2 score was 80.11%. It is crucial for systems with complex models to be transparent, explainable, and interpretable. For this reason, the model with the best performance was transformed into  
Figure 5: SHAP Results Based on CS. an explainable form using the LIME and SHAP in XAI methods, and the model was made understandable and interpretable for everyone. In addition, the analysis highlighted the feature importance for different classes and illustrated the decision-making path followed by the model. Our study presents a model that can be an alternative to the traditional CRA systems calculated based on credit history data. This model, developed using data from people's bill payment habits, can be employed as a risk assessment tool for individuals without a credit history or as a supplementary resource for those with existing credit scores. The incorporation of XAI methods in the study can be used as an important parameter for credit providers. Since credit providers can transparently observe the decision-making process and underlying logic of the model, the model can contribute positively to the process in terms of control and security. The transformation of the machine learning model into an explainable and interpretable format addresses concerns related to reliability, transparency, fairness, and ethics--key principles of XAI.