ATM Anomaly Detection With False-Positive Filtering and Resolution

The system segments anomaly detection into distinct modules: data collection from ATMs, preprocessing to remove false positives, anomaly detection logic application, and resolution model execution. Each module handles a specific aspect of the anomaly management process, improving overall system efficiency while maintaining manageable complexity through functional separation.

The system performs preliminary actions by pre-processing data to remove false positives before anomaly detection, and by pre-training resolution models on historical anomaly data. This preliminary preparation reduces the computational burden during actual anomaly events, improving response time and productivity.

The anomaly detection logic is segmented into multiple independent rules that evaluate different aspects of ATM transactions separately. Each rule focuses on specific anomaly patterns (e.g., unusual transaction amounts, unusual locations, unusual timing), allowing high detection precision while keeping individual rule complexity low and manageable.

The system applies multiple detection rules to each transaction, using comprehensive coverage of potential anomaly patterns. Even though individual rules may have varying levels of specificity, the collective application of multiple rules achieves high overall detection accuracy by catching different types of anomalies that single rules might miss.