Cash Flow Optimization in the ATM Network
Challenge
Reduction in operating costs for provisioning cash machines.
The actual daily data of ATM cash withdrawal was used for further data analysis. We utilized Gradient Boosting Regressor to build an ML-model .
Reduction in operating costs for provisioning cash machines.
The actual daily data of ATM cash withdrawal was used for further data analysis. We utilized Gradient Boosting Regressor to build an ML-model .
Solution
The solution comprised three steps. The first stage: data evaluation; setting the requirements and success criteria; data loading, depersonalization and data enrichment; experiment procedure agreements. The second stage focused on: segmenting the research objects; training, testing and evaluating the quality of the model. The third stage was the implemention of: automated data loading or model deployment in the customer’s environments; regular quality control by A/B testing; technical support of the model and optimization of new data entry.
The solution comprised three steps. The first stage: data evaluation; setting the requirements and success criteria; data loading, depersonalization and data enrichment; experiment procedure agreements. The second stage focused on: segmenting the research objects; training, testing and evaluating the quality of the model. The third stage was the implemention of: automated data loading or model deployment in the customer’s environments; regular quality control by A/B testing; technical support of the model and optimization of new data entry.
Results
Implementation of automated cash demand forecasting with an error range of 0.01% to 3.5%. Great reduction in operating costs by lowering the amount of allocated funds up to 30%, cashback up to 40%, out-of-cash downtime up to 0.2%.
Implementation of automated cash demand forecasting with an error range of 0.01% to 3.5%. Great reduction in operating costs by lowering the amount of allocated funds up to 30%, cashback up to 40%, out-of-cash downtime up to 0.2%.