Compliance annotation
Every auto-close and escalation carries reasoning and evidence. Aligns with AML investigation and case-management expectations.
Security & architecture briefBanking
Transaction monitoring and fraud alert triage
Review flagged transactions, cross-reference customer and transaction history, and escalate only genuine alerts, reducing false-positive workload by 60%.
The challenge
90%+ of fraud and AML alerts are false positives. Analysts spend their shifts clearing obvious non-issues, burn out, and genuine fraud slips through in the noise. Hiring more analysts is expensive and doesn't scale.
How Lunnoa runs it
- 1
Ingest
Pull the documents, events, or system records that start this process.
- 2
Analyse
AI agents pick up flagged transactions, cross-reference customer profiles, transaction history, device signals, and external data, and produce a structured evidence pack for each alert.
- 3
Decide or escalate
Clear cases continue under policy; exceptions go to a person with a structured pack.
- 4
Write back and prove
Update systems of record and keep an attributable trail for review.
Systems touched
- transaction monitoring
- CRM
- device signals
- case management
In the product
Step-by-step agent behaviour
Pull the documents, events, or system records that start this process.
Outcomes
- 60% reduction in false-positive workload
- Faster response to genuine fraud: minutes, not hours
- Consistent decisioning: no analyst-to-analyst variance
- Full evidence trail for every decision
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Für Teams, die Infrastruktur bereits ernst nehmen.
- CTOs und Platform-Teams, die die Passung zur eigenen Referenzarchitektur prüfen
- Security und Compliance mit Fokus auf Datenresidenz und Zugriffskontrolle
- Operations-Teams, die nachvollziehbare Läufe brauchen, keine Black-Box-Automatisierung
- Builder, die unbegrenzte Nutzung unter einer Pauschallizenz wollen