Multi-factor Digital Transmission Screening for Anomaly Detection

The patent replaces manual review (mechanical human operation) with an automated machine learning-based detection system. The system uses trained models to analyze digital transmissions, extracting features and generating anomaly scores automatically, thereby maintaining high detection accuracy while achieving the throughput necessary to handle millions of communications.

The detection system performs self-service by automatically analyzing transmissions without human intervention. The machine learning models independently evaluate features, compare against learned patterns, and generate detection results, enabling the system to maintain both accuracy and productivity without requiring manual review of each transmission.

The system applies local quality by treating anomalous and non-anomalous transmissions differently. Non-anomalous transmissions pass through with minimal intervention, maintaining seamless delivery, while only potentially anomalous transmissions undergo detailed analysis and may be diverted for further review, thus preserving normal communication flows while preventing data exfiltration.

The system applies partial action by not screening every transmission with the full analytical process. Instead, it uses initial filtering and anomaly scoring to identify only those transmissions requiring detailed review, applying the more resource-intensive analysis selectively to maintain both security and seamless delivery for the majority of communications.

The detection process is segmented into multiple stages: initial feature extraction, anomaly scoring using trained models, threshold-based filtering, and selective detailed analysis. This segmentation allows the system to apply computational resources efficiently, performing lightweight operations on all transmissions and reserving resource-intensive analysis only for potentially anomalous cases, thereby maintaining high accuracy while managing computational load.

The system performs preliminary action by pre-training machine learning models offline and pre-computing feature extraction pipelines. During runtime, these pre-prepared models and features enable rapid anomaly scoring without requiring heavy computational resources for each transmission, thus achieving high detection accuracy with reduced real-time computational demands.