AML, fraud andinternal fraud,explained.
Detection systems for anti-money laundering, transactional fraud and internal fraud — built on explainable models that survive regulatory review.
Designed to scale globally.
Scientific AI for fraud and AML — incorporating the audit and model risk DNA we already apply at financial institutions.
The problem everyone knows
Static rules, opaque scores, false positives that saturate analysts, and models that don't explain the why of each alert to auditors or regulators.
- ●89% of FIs prioritize explainability in fraud (Feedzai 2025)
- ●Enterprise competitors: months of integration and labeled data
- ●Internal fraud: a niche neglected by most vendors
Native Explainability
Auditable causal reasoning — not an opaque score.
Plug & Play
From data to UAT in weeks, not months.
Day 0 Detection
Anomalies from day one — no months of labeled fraud required.
Precision +5×
<1 false positive per real fraud vs published ~10:1 benchmarks.
Coverage
Transactional Fraud
- Account takeover · transactional fraud
- Identity theft · web & mobile channels
Anti-Money Laundering
- 17 laundering typologies
- Smurfing · structuring · placement · integration
Internal fraud
- Query · spending · messages · camera monitor
Bring us the decision your current stack cannot explain
We will map the decision, the data, the constraints and the product path before recommending a build.