Methodology
Academic theory.Grounded in industry context.
Neadl applies advanced anomaly detection methods that were previously practical only through bespoke data science work, then adds proprietary influence calculations to make the results understandable.
Proven modelling foundations
Use advanced techniques without commissioning a custom model for every new dataset.
Automated data readiness
Neadl prepares fields for modelling so reviewers do not need to make technical classification and encoding decisions.
Influence, not just scores
Reviewer-friendly explanations identify the parts of a record that pushed it away from expected behaviour.
Isolation Forest
Short paths expose unusual records.
Model family
Isolation Forest
Ensemble
4 sample trees
Signal
short path depth
Tree 01
2
path depth
Tree 02
7
path depth
Tree 03
3
path depth
Tree 04
9
path depth
Records isolated in fewer splits receive stronger anomaly signals across the ensemble.