Payment Status Classification Invoice Bank Using Logistic Regression and Random Forest

Abstract

Payment management is an essential aspect of a bank’s financial operations, particularly in ensuring the smooth execution of procurement transactions for goods and services. The invoice, as an official document, plays a role in determining whether a transaction can be processed promptly or experiences a delay. Despite its central role, empirical research exploring the factors influencing invoice payment status remains limited, especially within the context of banking institutions. This study aims to analyze the factors that affect invoice payment status based on company type, procurement type, and invoice value. The methods employed include logistic regression and random forest to compare the classification performance of both approaches. The analysis reveals that procurement type and invoice value significantly influence payment status, with invoice value emerging as the most dominant variable based on the smallest p-value. In the random forest model, invoice value also ranks highest in terms of variable importance. In terms of accuracy, the random forest model outperforms logistic regression, achieving an accuracy of 94.47% compared to 59.30%. Although both methods yield similar precision (approximately 97%), random forest demonstrates a substantially higher recall (97.41%) and F1-score, whereas logistic regression records a recall of only 69.19%. These findings suggest that random forest is a more effective method for predicting payment status and holds strong potential for supporting data-driven decision-making in bank payment management systems