Client and Project Introduction
A leading regional financial institution operating across Jordan and Egypt set out to enhance how it manages ATM cash flow across its vast network. With high customer expectations, fluctuating cash demand, and operational costs on the rise, the organization aimed to transform its cash management model through intelligent automation.
Service / Solution Benefits
MAGNOOS, an affiliate of MDS System Integration Group, delivered a machine learning–driven cash optimization solution that reshaped the bank’s ATM operations with real-time forecasting, smart scheduling, and AI-powered insight.
Customer Needs, Challenges, and Objectives
The bank faced multiple challenges in its ATM operations:
- Frequent cashouts during peak periods, affecting customer satisfaction
- Excessive cash reserves tied up in ATMs, resulting in idle capital
- High logistical costs due to frequent cash replenishment trips
- Limited visibility into how agent performance and delivery schedules affected efficiency
The bank’s goal was to build a forecasting model that could predict cash needs with high accuracy, optimize replenishment schedules, and free up capital for better use.
Key Highlights of the Solution:
- Custom machine learning model (GBDT) trained on historical ATM data
- Forecasts ATM cash needs with over 99% accuracy, one week in advance
- Integrated external variables such as holidays, economic indicators, and geographic demand
- Works seamlessly with any ATM or CIT vendor infrastructure
- Dynamic optimization of replenishment cycles and delivery routes
Key Results:
- 99% reduction in ATM cashouts, improving customer satisfaction
- $600 additional gross profit per ATM per month by freeing idle cash for lending
- 22% reduction in cash replenishment trips, lowering operational costs
- Enhanced decision-making with real-time data dashboards and predictive insights