Predictive Maintenance for a Global Cash-Logistics Network
Thousands of connected ATMs and intelligent safes processing hundreds of millions of transactions — replacing reactive servicing with predictive intelligence.
The Challenge
Unplanned downtime on a financial endpoint is expensive and reputation-damaging. Schedule-based preventive replacements were premature; reactive repairs were late. The fleet needed a real-time source-of-truth that could forecast failures before they happened.
The Architecture
What we built.
Fleet Telemetry Pipeline
Device operational data and service histories streamed into a central data lake; ETL, profiling, cleaning, and synthetic-data generation for rare failure modes.
Predictive Model
Deep neural network on engineered features (MTBF, MTTR, component-replacement frequency, operational event sequences) — time-based splits, Bayesian hyperparameter search, early stopping.
Production Delivery
REST API, monitoring dashboards, and field-test loops comparing predictions to actual failures — continuously retrained as the fleet evolves.
Business Value
What changed for the customer.
- Predictive recommendations replace reactive service calls — field teams arrive before a device fails.
- Thousands of devices and hundreds of millions of transactions monitored in real time from a single source-of-truth.
- Measurable reduction in false positives and missed failures, improving with every retraining cycle.
- Remote management across a global fleet — no more regional blind spots.
