## 3 Materials and Methods
### Dataset  
The dataset used in this study was prepared by analyzing real data from a payment center in Turkey, covering the period between January 2021 and January 2022. First, individuals' bill payment transactions were analyzed. Then, the features listed in Table 2 were extracted by calculating person-based payment habits, and the dataset to be used in this study was formed. The dataset contains no personal or sensitive information and consists of 13 features (12 inputs, 1 output) with a total of 42,117 samples. The credit risk feature, used as a label, has a categorical structure consisting of 10 classes. This feature, which indicates the real credit risk of people, is calculated by traditional methods and is based on real data obtained from the CS provider institution. The dataset's numerical variables' descriptive statistics are shown in Table 3. These consist of skewness and kurtosis values, count, mean, standard deviation, and the five-number summary (min, 25 th percentile, median, 75 th percentile, and max). Strong right-tailed distributions with outliers are shown by variables like TOTAL_PAYMENT_AMOUNT and PAYMENT_AMOUNT_AVG, which show exceptionally high kurtosis and high positive skewness (25.06 and 19.70, respectively). On the other hand, AGE exhibits a distribution \begin{table}
\begin{tabular}{l l l} \hline \hline
**Feature Name** & **Type** & **Description** \\ \hline GENDER & Categorical & Customer gender \\ \hline AGE & Numerical & Customer age \\ \hline PRODUCT\_CODE & Categorical & Product code depending on the bill type \\ \hline INVOICE\_TYPE & Categorical & Paid bill type (electricity, water, gas, telephone, satellite, internet) \\ \hline CASH\_PERCENTAGE & Numerical & Cash paid bill percentage \\ \hline NUMBER\_OF\_INVOICES\_PAID & Numerical & Number of bills paid in the last year \\ \hline TOTAL\_PAYMENT\_AMOUNT & Numerical & Total amount paid \\ \hline PAYMENT\_AMOUNT\_AVG & Numerical & Average of bill amount paid in the last year \\ \hline LATEST\_PAYMENT\_RATE & Numerical & Rate of bills paid after the last \\ \hline AVG\_DAYS\_PAID\_LATE & Numerical & Average number of days of \\  & & payments past the last \\ \hline AVG\_DAYS\_PAID\_EARLY & Numerical & Average number of days paid \\  & & before the last payment date \\ \hline NUMBER\_OF\_INVOICE\_TYPES & Categorical & Number of bill types registered \\  & & under the customer \\ \hline
**CREDIT\_SCORE** & **Categorical** & **Real Credit Score (1–10, 1:** **Worst—10: Best)**. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset Detail. Figure 1: Data Processing Flow. that is almost symmetrical, has a low skewness (\(\sim\)0.08), and exhibits a modest platykurtic characteristic. These traits suggest that proper normalization or transformation is required when training the model. To investigate the linear relationships between numerical variables, the Pearson correlation analysis was performed (Table 4). The statistical significance of the observed associations was evaluated using two-tailed significance testing with an alpha threshold of 0.05. Total Payment Amount (TOT_PAY) and Average Payment Amount (AVG_PAY) were shown to be highly positively correlated (r = 0.816, \(p\) < 0.001). This implies multicollinearity and shows that when the total is already part of a model, the average payment variable can be unnecessary. Additionally, TOT_PAY and Number of Invoices Paid (N_INV) had a somewhat good association (r = 0.318, \(p\) < 0.001), suggesting that, while not always the case, the frequency and size of payments tend to coincide. About 17.4% of the variation in credit scores was explained by the Cash Payment Percentage (CASH%), which showed a moderate to strong positive connection with Credit Score (CREDIT) (r = 0.417, \(p\) < 0.001) (R2 = 0.174). CREDIT and Late Payment Rate (LATE%) showed a substantial negative connection (r = \(-\)0.529, \(p\) < 0.001), indicating that timely payment behavior is a significant signal of greater creditworthiness, explaining 28.0% of the total score variance (R2 = 0.280).