Enhancing Banking Services through Data-Driven ATM Placement Strategies: A Case Study of PT Bank Rakyat Indonesia

Abstract

This research optimizes the placement strategy for new Automated Teller Machines (ATMs) at PT Bank Rakyat Indonesia (BRI). The study employs a data-driven methodology that integrates data preparation, clustering techniques, and coefficient calculations to identify the most suitable ATM locations. This approach goes beyond static models by incorporating customer behavior and transaction data.

The methodology utilizes a two-pronged approach. The first phase assesses the potential of various districts for ATM placement, considering factors such as demographics, transaction patterns, and competitor presence. K-means clustering is then employed to prioritize districts with the highest potential for benefiting from additional ATMs. The second phase involves calculating coefficients by analyzing correlations between existing ATM distribution and district potential scores. This analysis provides data-driven recommendations for the optimal number of new ATMs in each district.

By leveraging robust methodological framework, this research offers valuable insights for strategic decision-making and resource allocation. The proposed approach can enhance banking service accessibility across diverse regions, improve customer satisfaction, and contribute to the optimization of BRI's ATM network.