AI AML Transaction Monitoring
Fewer false positives, faster investigations and SBV-aligned SAR workflows — AI-driven AML monitoring layered on top of your existing scenarios and core data.
Overview
BancoOS ingests transactions from every rail, runs your scenarios and behavioural ML in parallel, and delivers a prioritised alert queue with full explainability.
Industry challenges
- Rules-only monitoring generates 90%+ false positives.
- Investigators drown in low-quality alerts.
- Case evidence is spread across systems.
- SBV expects auditable, defensible decisioning.
The BancoOS solution
Behavioural ML scores alerts using customer, peer-group and network context. Copilot lets investigators query cases in natural language. SAR workflows enforce four-eyes review and export in SBV-aligned formats.
Workflow diagram
Business outcomes
- 40–70% fewer false positives.
- 2–3× investigator throughput.
- Faster time-to-detect on true positives.
- Defensible, auditable decisioning.
Integrations
Core banking, card, wallet and remittance systems via API, file or event streams.
Security & compliance
Tenant isolation, encryption, RBAC, model-risk documentation and in-region hosting.
Frequently asked questions
›How does BancoOS reduce AML false positives?
Machine-learning models score alerts using behavioural context (customer, peer group, network), cutting typical false-positive rates by 40–70% versus rules-only monitoring.
›Does BancoOS replace our AML scenarios?
No. It augments them — running your existing scenarios plus ML models, then prioritising alerts for investigators.
›How are suspicious activity reports handled?
Alert cases flow into a SAR workflow with evidence packs, four-eyes review and export in SBV-aligned formats.
›Can investigators query in natural language?
Yes. The AI Copilot answers case questions grounded in transactions, customer data, prior alerts and documents.
›Which transaction sources are supported?
Core banking, card, wallet, remittance, correspondent and merchant acquiring — via API, file or event streams.
›Does BancoOS support network / link analysis?
Yes. Entity resolution builds customer networks so investigators can see counterparties, common addresses and shared devices.
›How is model explainability handled?
Every alert score carries feature attributions; models are documented for model-risk management review.
›How long to deploy?
8–12 weeks to production for the first typology set, with progressive expansion.
›Is data used for training?
No customer data is ever used to train foundation models; behavioural models are tuned per-tenant with your data staying in your tenant.
›What ROI do compliance teams see?
Typical results: 40–70% fewer false positives, 2–3× investigator throughput, and faster time-to-detect on true positives.
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