Optimizing Cash Management in ATM Networks using Time Series Models and Spatial Analysis

Effective cash management across ATM networks is essential for banks to maintain optimal service levels. However, in ABB, the largest state bank of Azerbaijan, cash management is conducted manually. The main objective of this thesis is to develop an optimal strategy for ABB’s ATM cash demand forecast by integrating time series models and spatial analysis. The significance of this study is that, with the help of a robust strategy, the bank will reduce operational costs, improve service levels, increase customer satisfaction, and enhance profitability. Cash demand forecasting experiments were conducted on ABB’s ATMs to achieve those goals. Various methods were used to build eight different models. Different time series, machine learning models, and Meta’s Prophet model were used. The best-performing model was SARIMAX, with SMAPE score of 5.89% and RMSE score of 83537. Next, spatial analysis was performed by grouping ATMs into five distinct sub-regions based on their distance from the city center. This analysis was done for Baku city to consider the various spatial factors influencing cash demand in different locations. Then, one ATM from each region was selected, and the cash demand for the last nine months was forecasted using SARIMAX. The MAPE score spans between 4.7% and 6.6% which demonstrates that the model is reliable even with different locations, populations, and atm distributions. The forecasted MAPE scores will be utilized as model calibration factors to adjust the fluctuations of the model's predictions in production. The integration of forecast results with spatial analysis to formulate strategies represents the novelty presented in this thesis.