## 3 Materials and Methods
### Dataset
To a lesser degree, Average Early Payment Days (AVG_EARLY) also showed a positive correlation with CREDIT (r = 0.295, \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Feature Name** & **AGE** & **CASH\%** & **N\_INV** & **TOT\_PAY** & **AVG\_PAY** & **LATE\%** & **AVG\_LATE** & **AVG\_EARLY** & **N\_TYPES** & **CREDIT** \\ \hline
**AGE** & 1.000 & 0.096 & \(-\)0.006 & \(-\)0.042 & \(-\)0.051 & \(-\)0.154 & \(-\)0.027 & 0.110 & \(-\)0.062 & 0.231 \\ \hline
**CASH\%** & 0.096 & 1.000 & 0.037 & 0.026 & 0.012 & \(-\)0.276 & \(-\)0.053 & 0.076 & \(-\)0.010 & 0.417 \\ \hline
**N\_INV** & \(-\)0.006 & 0.037 & 1.000 & 0.318 & 0.053 & 0.003 & 0.001 & 0.025 & 0.114 & 0.072 \\ \hline
**TOT\_PAY** & \(-\)0.042 & 0.026 & 0.318 & 1.000 & 0.816 & 0.025 & \(-\)0.007 & \(-\)0.017 & 0.041 & 0.032 \\ \hline
**AVG\_PAY** & \(-\)0.051 & 0.012 & 0.053 & 0.816 & 1.000 & 0.033 & \(-\)0.012 & \(-\)0.039 & 0.006 & 0.018 \\ \hline
**LATE\%** & \(-\)0.154 & \(-\)0.276 & 0.003 & 0.025 & 0.033 & 1.000 & 0.173 & \(-\)0.253 & \(-\)0.087 & \(-\)0.529 \\ \hline
**AVG\_LATE** & \(-\)0.027 & \(-\)0.053 & 0.001 & \(-\)0.007 & \(-\)0.012 & 0.173 & 1.000 & \(-\)0.006 & \(-\)0.020 & \(-\)0.107 \\ \hline
**AVG\_EARLY** & 0.110 & 0.076 & 0.025 & \(-\)0.017 & \(-\)0.039 & \(-\)0.253 & \(-\)0.006 & 1.000 & 0.032 & 0.295 \\ \hline
**N\_TYPES** & \(-\)0.062 & \(-\)0.010 & 0.114 & 0.041 & 0.006 & \(-\)0.087 & \(-\)0.020 & 0.032 & 1.000 & 0.101 \\ \hline
**CREDIT** & 0.231 & 0.417 & 0.072 & 0.032 & 0.018 & \(-\)0.529 & \(-\)0.107 & 0.295 & 0.101 & 1.000 \\ \hline \hline \end{tabular}  
* AGE: customer age, CASH%: cash paid bill percentage, N_INV: number of invoices paid in the last year, TOT_PAY: total amount paid, AVG_PAY: average payment amount, LATE%: latest payment rate, AVG_LATE: average number of days paid late, AVG_EARLY: average number of days paid early, N_TYPES: number of invoice types, CREDIT: credit score (1\(-\)10).  
\end{table} 
Table 4: Correlation Analysis of Numerical Variables. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Feature Name** & **Mean** & **Std Dev** & **Min** & **25\%** & **50\%** & **75\%** & **Max** & **Skewness** & **Kurtosis** \\ \hline ACE & 52.90 & 11.30 & 20.0 & 45.0 & 52.0 & 61.0 & 96.0 & 0.09 & \(-\)0.26 \\ \hline CASH\_PERCENTAGE & 0.80 & 0.29 & 0.0 & 0.62 & 1.00 & 1.00 & 1.00 & \(-\)1.29 & 0.47 \\ \hline NUMBER\_OF\_INVOICES\_PAID & 10.52 & 5.59 & 1.0 & 8.0 & 11.0 & 13.0 & 184.0 & 2.91 & 37.29 \\ \hline TOTAL\_PAYMENT\_AMOUNT & 2572.68 & 6181.46 & 0.01 & 822.09 & 1595.55 & 2952.72 & 34,6076.06 & 25.06 & 985.09 \\ \hline PAYMENT\_AMOUNT\_AVG & 233.90 & 371.89 & 0.01 & 99.22 & 160.96 & 280.32 & 21,476.87 & 19.70 & 695.27 \\ \hline LATEST\_PAYMENT\_RATE & 0.32 & 0.31 & 0.0 & 0.0 & 0.28 & 0.52 & 1.00 & 0.64 & \(-\)0.67 \\ \hline AVG\_DAYS\_PAID\_LATE & 7.35 & 36.07 & 0.0 & 0.0 & 3.56 & 9.33 & 5443.00 & 102.71 & 13,782.49 \\ \hline AVG\_DAYS\_PAID\_EARLY & 5.81 & 4.27 & 0.0 & 2.60 & 5.67 & 8.50 & 94.75 & 0.84 & 5.36 \\ \hline \hline \end{tabular}  
\end{table} 
Table 3: Dataset Numerical Variables Statistics. \(p\) < 0.001). This lends more credence to the idea that financial assessments are enhanced by payment discipline. AVG_EARLY (r = 0.110, \(p\) = 0.02) and CREDT (r = 0.231, \(p\) < 0.001) showed a moderately positive connection with age (AGE), suggesting that older consumers typically had better credit scores and more disciplined payment habits. Younger people are more prone to postponing payments according to the statistically significant negative association between AGE and LATE% (r = \(-\)0.154, \(p\) < 0.001). A moderately negative association (r = \(-\)0.253, \(p\) < 0.001) between LATE% and AVG_EARLY showed a trade-off in payment behavior. Early payers are less likely to be late payers, and vice versa. The frequency of delays (as measured by LATE%) has a greater influence than the length of delays, according to the Average Days Paid Late (AVG_LATE), which has comparatively weak and non-significant relationships with the majority of factors.