Customer Segmentation of Cash Management System Using K-Means Clustering

The effective financial management is essential for running successful business operations. In the banking context, the Cash Management System (CMS) facilitates real-time, automated transaction management. PT Bank Rakyat Indonesia (Persero) Tbk., as one of Indonesia’s largest banks, has implemented CMS since 2009. Despite its benefits, challenges persist, such as customer transactions outside regular working hours and difficulties in segmenting customers based on transaction volume and frequency. This study aims to address these issues by clustering BRI CMS users using the K-Means Clustering method, following the CRISP-DM framework. The research utilized transaction data of 2,727 users from January 2021 to April 2022. Data preparation involved cleaning anomalies and converting non-numeric values to numeric formats. Using the Elbow method, the optimal number of clusters was determined, resulting in three distinct user segments. The clustering revealed actionable insights, such as identifying high-value customers for targeted marketing and improving service strategies. This research offers a novel application of K-Means Clustering and CRISP-DM to CMS data management, contributing to better customer segmentation and strategic decision-making. These findings can help banks optimize resources, improve customer satisfaction, and enhance overall transaction efficiency.