ATM Cash Demand Forecasting: Fewer Cash-Outs, Less Idle Cash
ATM replenishment that runs on fixed schedules leaves machines empty on salary days and around Eid while others sit over-filled. Stixor forecasts demand per ATM 14 days ahead, refreshes daily, and converts every forecast into a load amount and replenishment schedule.

14-day
Forecast Horizon
Daily
Forecast Refresh
40%
Reduction in ATM Cash-Outs
25%
Reduction in Cash Held at ATMs
Business Overview and Strategic Direction
EXECUTIVE SUMMARY
ATM replenishment at many banks still runs on fixed schedules and branch judgement. Machines run dry at month-end, on salary days and around Eid, while others sit over-filled, tying up idle cash, insurance cover and cash-in-transit trips. Every cash-out is a customer complaint, and every over-fill is cost of funds.
Stixor built a forecasting and planning solution that predicts withdrawals for each ATM from transaction history and calendar effects such as salary cycles, public holidays, Eid and harvest season, plus location and local events. Each forecast becomes a recommended load amount and replenishment schedule, refreshed daily over a 14-day horizon. The same models extend to branch vault cash and cash-centre planning, giving treasury and operations one shared view.

Operational Challenges
Cash demand at each ATM swings with paydays, holidays and local events, but replenishment plans do not. Key challenges:
PROBLEM STATEMENT
Cash-Outs at Peak Times
Machines run dry at month-end, on salary days and around Eid, causing customer complaints and lost transactions.

Idle Cash and Cost of Funds
Over-filled ATMs tie up working capital and raise insurance cover, so cash that could earn a return sits in machines.

Calendar-Blind Scheduling
Fixed replenishment schedules and branch judgement cannot account for salary cycles, public holidays, harvest season or local events.

Costly Cash-in-Transit Trips
Unplanned and emergency refills add cash-in-transit trips, and trips that fall on weekends or holidays cost more.

Demand Forecasting and Replenishment Optimization Platform
A forecasting and planning platform that predicts cash demand per ATM and turns it into load amounts, schedules and alerts that treasury and operations can act on.
Our Solution
Calendar-Aware Demand Forecasting
Gradient-boosted and time-series models (Prophet, SARIMA) forecast net withdrawals per ATM from history, salary cycles, holidays, Eid, harvest season and local events, refreshed daily over a 14-day horizon and tracked on MAPE.
Replenishment Planning
Each forecast becomes a recommended load amount, cassette denomination mix and replenishment date, balancing cash-out risk against idle cash, insurance limits and cash-in-transit (CIT) cost.
Treasury and Operations View
One dashboard shows per-ATM forecasts, low-cash alerts and planned refills, with a full audit trail of forecasts, overrides and actual withdrawals.
01
Demand Modeling
- Per-ATM and cluster-level forecasts that share patterns across machines with similar demand
- Calendar features for salary days, month-end, public holidays, Eid and harvest season
- Location and local-event signals for markets, campuses and seasonal sites, plus deposit flows for cash-recycling ATMs
02
Replenishment Optimization
- Load amounts sized to forecast demand plus a safety stock per ATM, within cassette capacity and insurance limits
- Replenishment schedules that cut emergency and weekend refills
- Route-ready refill orders for CIT partners
03
Alerts and Visibility
- Low-cash and over-fill alerts before problems reach customers
- Forecast versus actual tracking per ATM and region
- Overrides recorded with reasons for full auditability
04
Extension Beyond ATMs
- Same models applied to branch vault cash
- Cash-centre demand planning across the network
- Continuous retraining as withdrawal patterns change
Business Outcomes
IMPACT & RESULTS
Compact Performance Metrics
14-day
Forecast Horizon
Daily
Forecast Refresh
40%
Reduction in ATM Cash-Outs
25%
Reduction in Cash Held at ATMs
Operational Efficiency
<10%
Forecast Error per ATM (MAPE)
35%
Reduction in Emergency Replenishments
20%
Reduction in Cash-in-Transit Trips
99%+
ATM Cash Availability
Technology Stack
TOOLS USED
Python / PyTorch
TensorFlow
PostgreSQL
Apache Kafka
Docker / K8s
Grafana
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