  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.
