ATM Allocation Using Decision Tree-Based Algorithms

Automated teller machines (ATM's) make it possible for customers to fulfill their financial operations easily and reduces the workload of bank branches if they are placed in convenient locations. Banks need to have ATMs allocated in favorable locations regarding customer concerns. In this study, the ATM allocation problem is handled using decision tree-based algorithms. To solve the problem, a machine learning algorithm should learn the characteristics of each defined region and understand factors affecting the business performance. Therefore, a grid system is designed by dividing Turkey by imaginary lines. Imaginary lines constitute small grids passing through each one-thousandth of a latitude degree and one-thousandth of a longitude degree. For each grid rectangle, the characteristics of the customers living or wandering there, the point of interest locations around the area, and the existence of the competitors' ATMs are determined. Then, algorithms are trained and scored using decision tree-based algorithms. To decide suitable grid areas for installment, the business value is calculated for each grid. A heat map presenting the scores of the whole country is created for visualization purposes. The proposed framework can be used to better allocate ATMs all around in Turkey.