Anomaly detection systems in fields like cybersecurity, fraud detection, and healthcare often act as black boxes—they flag unusual patterns but don’t explain why. This creates challenges: analysts distrust alerts they can’t understand, investigations become inefficient, and compliance requirements (like GDPR) may be violated. One way to address this is by developing an explainable AI system that reveals not just which features contributed to an anomaly but also the sequence in which they became significant.

How Sequential Explanations Work

For a random forest model, this could involve tracing the decision path through the trees that most influenced the anomaly score. At each split, the system would identify:

• The feature used and its threshold value
• How much the split contributed to the final anomaly score

This sequence would highlight early warning signs, decisive factors, and even features that initially suggested normality but were overridden. Unlike general tools like LIME or SHAP—which provide static feature importance—this approach mirrors how human analysts investigate anomalies: by following a progression of evidence.

Alignment with Stakeholder Needs

The system would serve security analysts, fraud investigators, and quality control engineers who need actionable, transparent explanations without deep ML expertise. Their incentives align well:

• Analysts want faster, more confident investigations
• Data teams need interpretability to improve models
• Compliance officers require documentation for audits

An MVP could start with a Python package for Jupyter notebooks, later expanding to a web interface and integrations with monitoring systems. Early validation might involve testing on public datasets (like NAB or KDD Cup) to confirm that sequential explanations reduce investigation time versus traditional methods.

Differentiation from Existing Tools

While tools like ELI5 or SHAP offer model-agnostic explanations, this approach would specialize in anomaly detection by emphasizing the "story" behind each alert. For edge cases where sequences aren’t meaningful, it could fall back to feature interaction scores. Potential monetization paths include enterprise licensing or a cloud-based explanation service tailored to high-stakes domains like fraud or industrial monitoring.