Cash Order Optimization
Predictive model for cash demand forecasting to optimize cash collection operations.
Challenge
A financial institution's network of cash service points consistently faced two problems: excess cash at some locations (frozen capital) and shortages at others (lost transactions). Manual cash collection planning failed to account for local factors such as holidays, payroll dates, weather conditions, and proximity to shopping centers.
Solution
The predictive model forecasts cash demand for each service point over a 1-to-14-day horizon. It accounts for day of week, seasonality, holidays, local events, and historical patterns. The system generates an optimal cash collection schedule that minimizes total operating costs.
Results
Technologies
Approach
Historical data analysis by location
Collecting and systematizing transaction data from each cash service point over several years.
Feature engineering: external factors
Incorporating features such as holidays, payroll dates, weather conditions, proximity to retail locations, and local events.
Forecasting model training
Building and validating time series models tailored to the specifics of each location.
Integration with the cash collection system
Automated generation of optimal collection schedules and integration with operational processes.
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