Howso: Leading the Charge in Anomaly Detection

John Emmert

The Data Proves It 

In recent benchmarks across multiple datasets, Howso’s anomaly detection outshined industry-standard methods—including OCSVM, Isolation Forest, CBLOF, LOF, ECOD, and DeepSVDD. 

Key results: Howso was the top performer in over 70% of tests—higher than any other solution, as well as in the top 3 in 100% of tests. 

Table 1: Benchmarking Howso’s anomaly detection algorithm against industry-standard anomaly detection methods, using dataset sourced from the Outlier Detection DataSets. The values represent F1 scores for each algorithm, and the highest F1 score for each dataset is italicized and highlighted.  

Why Howso is Best-in-Class


Unlike black-box AI models, Howso leverages Causal AI to provide more explainable, interpretable, and actionable anomaly detection. Traditional anomaly detection models often fail due to:

  1. Lack of Context – Traditional models can flag outliers but often miss more subtle, context-driven anomalies—especially inliers that occur within dense regions of “normal” data. Worse, they offer no explanation for why an anomaly is occurring, leaving users in the dark.
  2. Static Analysis – Many traditional models rely on predefined patterns that fail to adapt dynamically. Howso solves these challenges by applying causal reasoning, ensuring that businesses not only accurately detect anomalies but also understand their root causes and mitigate risks effectively.

The Future of Anomaly Detection is Here

With the latest improvements in our anomaly detection models, Howso continues to lead the industry in interpretable data analytics for real-world applications. Whether you’re in finance, healthcare, retail, or cybersecurity, our solutions provide unmatched value in safeguarding operations and uncovering hidden risks. Want to see Howso in action? Contact us for a demo today!