Location Optimization of ATM Networks

ATMs enable the public to perform €nancial 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 eciently 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 over€Šing. Œe major challenge in learning the model parameters is the lack of an objective function. Œe model is trained using a synthetic objective function using the dominant features returned from the k-means clustering algorithm in the local model. Œe results obtained from the energetic features using the models are encouraging.