Advanced time series forecasting system for optimizing ATM cash management and reducing operational costs while maintaining service availability
A major bank network struggled with inefficient ATM cash management across thousands of locations, leading to high operational costs from frequent refills and occasional service outages when machines ran out of cash. Their existing cash forecasting methods couldn't accurately predict demand patterns, resulting in either excessive cash holdings or customer service disruptions.
We developed a sophisticated time series forecasting model using ARIMA and seasonal decomposition techniques, combined with external factors like local events, weather patterns, and economic indicators. The system predicted cash demand for each ATM location with different forecasting horizons and optimized refill schedules to minimize costs while ensuring availability.
The time series model achieved 92% accuracy in predicting ATM cash demand across all locations and time periods. Operational costs were reduced by 28% through optimized cash distribution and reduced emergency refills. ATM uptime improved to 99.2%, significantly enhancing customer satisfaction while maintaining efficient cash inventory management across the network.