Financial services
Fraud and risk detection
Surface genuine fraud and risk signals while cutting false positives, with every alert explainable to an investigator and a regulator.
The problem
What this fixes.
Built for Financial-crime, risk and fraud leaders buried in false positives and unable to explain their models.
Rules-based systems generate false positives that bury the real signal and exhaust investigators.
Opaque scoring models cannot be explained to investigators or regulators.
New fraud patterns are caught late, after the loss has already happened.
How we deliver it
From problem to production.
01
Detect
Signals are combined to surface genuine anomalies, tuned to your loss profile rather than a generic default.
02
Explain
Every alert comes with the reasons it fired, so an investigator can act in minutes, not hours.
03
Prioritise
Alerts are ranked by expected loss and confidence, concentrating attention where it pays.
04
Adapt
Investigator feedback and new patterns feed back into the model under change control.
Outcomes
What good looks like.
0%
Reduction in false positives
0%
Faster alert investigation
0%
Alerts shipped with an explanation
Indicative ranges drawn from comparable engagements, measured against a pre-AI baseline. Your figures are set and tracked from your own data during delivery.
What you get
Shipped, owned, documented.
Every engagement hands over working software in your repositories, with the evidence to run and audit it.
Anomaly detection tuned to your loss profile
Explainable alerts with contributing factors
Risk-and-confidence alert prioritisation
Investigator feedback loop under change control
Model-risk audit pack and evaluation harness
Questions
What buyers ask first.
Yes. Every alert carries the factors that drove it, and the model ships with an audit pack and a versioned evaluation harness. Explainability is a design requirement here, not a feature added after the fact.
Investigator feedback and emerging patterns feed back into the model under change control, with each change regression-tested on the evaluation harness so a fix for one pattern does not quietly break detection of another.
Capabilities behind it