Anomaly Detection

Find Critical Signals in Digital Noise

Data Collection

Gathering relevant data from various sources throughout the organization provides the deep insights required to understand normal process and asset behavior patterns. Collection involves integrating both structured and unstructured information from locations like databases, logs, sensors and user feedback.

Pre-processing

Ensuring the quality and coherence of raw inputs for analysis involves cleaning data through techniques such as handling missing or incorrect values and resolving inconsistencies in naming conventions or formatting. Pre-processing also uses enrichment to add useful context through linking related information.

Model Training

Historical data is distilled into conceptualized patterns of normalcy using statistical algorithms, unsupervised or supervised machine learning depending on labeling availability. Reoccurring sequences, correlations and key performance indicators form the baseline of expected functionality.

Classification

New observations are scored or categorized in real-time by the intelligent models as normal or anomalous based on behavior divergences from the established norms. Both subtle and severe outliers are exposed above detection thresholds.

banking case 1

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Explainability

Rather than simplistic labels, WelfLab solutions provide explanatory evidence on the multiple contributing factors and contextual root causes behind anomalies through specialized causality analysis.

Scalability

Built on elastic cloud-native architectures, WelfLab components auto-scale horizontally to analyze and learn from data volumes in the petabytes while maintaining rapid detection velocity for time-sensitive detections.

Customization

WelfLab experts establish personalized detection tuning through an initial domain modeling process and regularly capture customer feedback to refine defined norms and the focus of relevant anomalies over time.

Expert Guidance

A assigned WelfLab solution adviser provides dedicated implementation consulting from proof of value through production deployment. Support also assists with ongoing optimization, sustaining value and addressing future needs.

Anomaly Detection

Find Critical Signals in Digital Noise

banking case 1

Case study 1

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