What is AI anomaly detection?

AI anomaly detection learns the shape of normal activity and flags departures from it, catching patterns fixed rules miss. Zabble compounds a bespoke rules-based decision engine with a model that learns over time, so detection improves while every flag still carries its reason, keeping the system auditable, not a black box.

What does an anomaly detection system do?

It monitors a high-traffic stream no human could realistically watch, applies detectors tuned to what counts as unusual for that business, and surfaces only the cases that matter, each with its rule, history, and suggested action. Every decision lands in an immutable audit trail keyed by case ID.

What risks can continuous monitoring catch?

Fraud in transactions, drift in operations, equipment failure, and outliers in performance, risks that share one shape: a tiny signal hidden inside an ocean of normal activity. Sampling misses them; continuous monitoring catches them at volume, dropping mean time to detection from days toward seconds.

How is this different from sampling or manual review?

Manual review and sampling only ever see a fraction of activity, so the events that hurt most are the ones missed. A continuous assurance engine reads the whole stream, flags only genuine exceptions, and hands investigators cases with the evidence already attached, instead of leaving them to triage false positives by hand.

Bespoke System Live

Continuous Assurance Engine

Background monitoring across a stream of activity no human could realistically watch, only the things that matter surface, with their evidence already attached.

Audit Anomaly Analytics

Industry: Banks, fintech, B2B SaaS, manufacturing & facilities
Best for: Operators sitting on a firehose of events where the costly few are buried among the routine many

In short

What is anomaly detection in business?

Anomaly detection in business is automated monitoring that watches a stream of activity, transactions, operations, sensor data, and flags what is unusual before it becomes a problem: the fraud, the error, the drift, the outlier. Zabble’s engine surfaces only the cases that matter, each with the rule, history, and suggested action attached.

How we work

We sit with your business. We find the operational problem costing you the most. We build the system that fixes it.

The Problem

Fraud, drift and equipment failure share one shape, a tiny signal inside an ocean of normal activity. By the time a human notices, the damage is already booked. The analyst, the engineer, the data lead, all of them dread finding out too late. Most teams cope by sampling, and the things they miss are the things that hurt.

What We Built

A single engine ingests the live stream, card transactions, CRM events, sensor telemetry. The right detector picks up each event by source. Every flag carries its rule, its history, and the suggested action. Every decision lands in an immutable audit trail, keyed by case ID.

What Changed

The risks that hurt most, fraud, drift, equipment failure, started getting caught at volume. Investigators stopped triaging false positives by hand. They now receive cases with their evidence already attached. Mean time to detection dropped from days to seconds.

What is continuous assurance?

A typical organization loses 5% of its revenue to occupational fraud every year, and the median scheme runs about 12 months before it is caught.

ACFE, Occupational Fraud 2024: A Report to the Nations

One system, three jobs.

Automation

Not the primary focus for this system.

Audit Trails

Every flag opens a case. The case holds the rule that fired, the surrounding history, the suggested action, and the operator who reviewed it. Written once, never edited. Disputes get answered by replay, not by recollection.

Explore Audit Trails

Anomaly Detection

Four detector families run in parallel. Rules catch hard thresholds. Statistical models catch drift. Pattern scoring catches fraud. Signal-processing reads vibration data. Each event picks the right detector for its source.

Explore Anomaly Detection

Analytics

A live sensitivity dial shows the trade-off between coverage and false positives. Weekly replays let the team count what would have been caught. The dial gets tuned against what the business actually wants to see.

Explore Analytics

FAQ

Frequently asked questions

What is AI anomaly detection?: AI anomaly detection learns the shape of normal activity and flags departures from it, catching patterns fixed rules miss. Zabble compounds a bespoke rules-based decision engine with a model that learns over time, so detection improves while every flag still carries its reason, keeping the system auditable, not a black box.
What does an anomaly detection system do?: It monitors a high-traffic stream no human could realistically watch, applies detectors tuned to what counts as unusual for that business, and surfaces only the cases that matter, each with its rule, history, and suggested action. Every decision lands in an immutable audit trail keyed by case ID.
What risks can continuous monitoring catch?: Fraud in transactions, drift in operations, equipment failure, and outliers in performance, risks that share one shape: a tiny signal hidden inside an ocean of normal activity. Sampling misses them; continuous monitoring catches them at volume, dropping mean time to detection from days toward seconds.
How is this different from sampling or manual review?: Manual review and sampling only ever see a fraction of activity, so the events that hurt most are the ones missed. A continuous assurance engine reads the whole stream, flags only genuine exceptions, and hands investigators cases with the evidence already attached, instead of leaving them to triage false positives by hand.

Related systems

What is anomaly detection in business?

What is continuous assurance?

One system, three jobs.

Frequently asked questions

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