A bank doesn’t have a label for every new fraud trick, but it knows what normal spending looks like for each customer. Anomaly detection builds a picture of “normal” and raises a flag when something sits far outside it: a purchase pattern, a sensor reading, a login location, a jump in an API’s error rate. That makes it useful exactly when bad events are rare, varied or new, which is common in imbalanced problems.
Methods range from simple to learned. A rule such as “more than three standard deviations from the average” is a start. scikit-learn documents learned options such as Isolation Forest and Local Outlier Factor, and separates outlier detection (the training data already contains some oddities) from novelty detection (train on clean data, flag anything new). Neural approaches reconstruct normal data and flag what they reconstruct badly. Security tools have long used the same idea; NIST’s intrusion-detection guide describes anomaly-based detection that compares activity with a profile of normal behavior.
The hard part is tuning. Flag too eagerly and analysts drown in false positives; too cautiously and real incidents slip by. “Unusual” also isn’t the same as “bad”: a holiday sales spike is an outlier that’s fine. In AI operations, the same techniques watch models in production, spotting shifts in inputs or outputs as part of AI observability and model drift monitoring. In episode 75 at 5:26, WisdomAI CPO Kapil Chhabra lists anomaly detection followed by root-cause analysis as an example of a finance task he wants AI agents to handle; that is his description of a product use case. WisdomAI sponsored this interview (0:52).
Hear it from the guest
“I want to do anomaly detection on this data and then go into root cause”
Quotes lightly edited to remove filler words.
Sources
- Google Machine Learning Glossary: anomaly detection — Defines anomaly detection as identifying outliers, with an example of a value far outside a feature's usual range; checked October 24, 2026.
- scikit-learn: Novelty and Outlier Detection — Distinguishes outlier detection (training data already contains outliers) from novelty detection (training data is clean), and documents Isolation Forest, Local Outlier Factor and One-Class SVM; checked October 24, 2026.
- NIST SP 800-94: Guide to Intrusion Detection and Prevention Systems — Describes anomaly-based detection, which compares activity against profiles of normal behavior, alongside signature-based detection; checked October 24, 2026.
