Enhancing White Label ATM Network Efficiency: A Data Science Approach to Route Optimization with AI

White-label automated teller machine networks are confronted with considerable operational issues, especially as a result of the intricate logistics involved in cash replenishment and maintenance. In this study, a data science strategy that makes use of artificial intelligence (AI) is proposed as a means of optimising route planning for automated teller machine (ATM) service operations. Through the utilisation of machine learning algorithms and advanced analytics, the study intends to accomplish the goals of lowering operational expenses and increasing service optimisation. The methodology entails analyzing historical data obtained from a top WLA operator. This data comprises records of cash withdrawals, maintenance activities, journey times, and service limits. The prediction of cash demand and the development of an AI-driven route optimisation model are both accomplished through the use of many tools, including regression models, time-series analysis, genetic algorithms, and neural networks. It has been demonstrated that there has been a significant reduction in trip lengths and times, which has resulted in cost savings and an improvement in service reliability. Those who operate automated teller machines and are interested in improving network performance through data-driven techniques will benefit greatly from the following study.