Executive Summary

The error reduction (measured via MAE) directly translates into more precise cash planning

The weekday seasonality view gives operations an immediate rule-of-thumb: which days require systematically higher limits or earlier refills.

The scatterplot reassures risk and analytics teams that the model is not just good on average, but behaves sensibly across low, medium, and high-demand days.

Business Problem & Why Forecast Accuracy Matters

For retail banks and ATM operators, cash logistics is one of the most capital-intensive and operationally sensitive processes. Each ATM must carry enough cash to avoid service disruptions, yet overstocking leads to excessive working capital, unnecessary CIT (cash-in-transit) operations, and higher insurance costs.

Even small improvements in forecast accuracy can translate into significant operational savings. A 10% reduction in forecast error may reduce annual cash logistics costs by 5–8%, depending on fleet size and replenishment frequency.

The client — a regional ATM operator — wanted a data-driven forecasting system that could outperform their existing rule-based predictions and enable

Our goal: produce a reliable 30-day ahead forecast of daily ATM cash withdrawals, with model transparency and automated deployment.

• fewer emergency replenishments,

• better liquidity planning,

• lower idle cash,

• and improved customer service availability.

Business Impact

From a business standpoint, this is the essence of BI-supported fraud and risk management:

• Even a relatively simple machine-learning model using basic transactional, calendar, and lagged features can materially outperform a naïve “yesterday = tomorrow” rule.

• The error reduction (measured via MAE) directly translates into more precise cash planning:

  • fewer emergency refills and stock-outs,

  • less idle cash trapped in ATMs,

  • smoother logistics for cash-in-transit teams.

• Operational efficiency — as manual review teams focus their effort exactly where it adds most value.The weekday seasonality view gives operations an immediate rule-of-thumb: which days require systematically higher limits or earlier refills.Operational efficiency — as manual review teams focus their effort exactly where it adds most value.

• The scatterplot reassures risk and analytics teams that the model is not just good on average, but behaves sensibly across low, medium, and high-demand days.

• The final chart summarizes the story for non-technical stakeholders: historical performance, model forecasts, and the qualitative message that “machine learning brings ATM cash forecasting under control.

This case study forms a concrete, data-driven example of how pyAxiom can help finance and operations teams move from static rules to intelligent, learning-based cash-flow forecasting – starting with a single ATM and easily scaling to large networks.