Case Study:ATM IncidentManagement System
Infominez helps businesses simplify operations with EaseFlow
Client Satisfaction Rating
Our clients consistently give us 5–star ratings for delivering reliable solutions and exceptional support.
42+
Hours Saved Monthly
On average, customers save more than 42 hours every month by using EaseFlow to automate and optimize their workflows.
Client Overview
Industry:
Business Challenge: A financial services provider managing thousands of ATMs nationwide needed to optimize cash replenishment. Unpredictable withdrawal patterns caused both overfilling (raising logistic costs) and stockouts (leading to customer dissatisfaction). They required a machine–learning forecasting system to predict daily withdrawals and improve efficiency.
Background
Objective: Develop a scalable forecasting system to predict daily ATM withdrawals using historical transaction data, reducing cash overfill and shortage events.
Scope: The project involved building ETL pipelines for large-scale data preprocessing, clustering ATMs to detect regional patterns, and testing machine learning models with external variables like holidays and seasonal trends.
Technologies Used:
Solution
Approach: An agile, data-driven development cycle was adopted. The main challenges were noisy transaction data, high ATM variability, and model accuracy. These were addressed with clustering (K-means + DTW), ETL automation, and model experimentation.
Methods & Strategies:
Key Highlights:
- Accurate daily predictions at large scale.
- ATM clustering refined accuracy.
- External features improved seasonal demand capture.
- Kafka prototyping prepared system for future real-time needs.
Results
Results: Reduced overfilling and stockout incidents by about 40%. Logistics costs decreased, and operational efficiency improved.
Metrics of Success:
40% fewer cash management errors.
Forecasts 30–35% more accurate than prior methods.
Transport costs reduced by around30%.
Improved customer satisfaction by reducing stockout events.
Value Delivered: Advanced ML, clustering, and scalable engineering created a reliable, transformative ATM cash prediction system in under six months. Advanced ML, clustering, and scalable engineering created a reliable, transformative ATM cash prediction system in under six months.
Lessons Learned
- Clustering was key to scaling across thousands of ATMs with varying demand.
- MLOps improved collaboration and reproducibility.
- Preprocessing noisy data was vital for stability.
- Kafka showed potential for future real-time deployment.
Conclusion
This project demonstrates how machine learning, clustering, and MLOps can modernize large-scale ATM cash management. By combining accurate forecasting with automated pipelines, the client reduced costs, improved forecasting precision, and enhanced customer experience. Future integration with real-time data streams will further strengthen scalability and responsiveness.
What Our Clients Are Saying
Our customers are our first priority and we keep them in mind with every
decision we make. Hear what they have to say.
