Location Optimization of ATM Networks

TL;DR

This paper presents a novel spatial modeling approach for ATM location optimization, combining local and global prediction models to improve transaction likelihood estimation and guide strategic ATM placement.

Contribution

Introduces a dual-model framework that captures spatial variance and trends for ATM location scoring, addressing the lack of an explicit objective function.

Findings

Encouraging results from energetic feature modeling.

Effective integration of local and global spatial features.

Potential for improved ATM placement strategies.

Abstract

ATMs enable the public to perform financial transactions. Banks try to strategically position their ATMs in order to maximize transactions and revenue. In this paper, we introduce a model which provides a score to an ATM location, which serves as an indicator of its relative likelihood of transactions. In order to efficiently capture the spatially dynamic features, we utilize two concurrent prediction models: the local model which encodes the spatial variance by considering highly energetic features in a given location, and the global model which enforces the dominant trends in the entire data and serves as a feedback to the local model to prevent overfitting. The major challenge in learning the model parameters is the lack of an objective function. The model is trained using a synthetic objective function using the dominant features returned from the k-means clustering algorithm in the local model. The results obtained from the energetic features using the models are encouraging.