Fraude Intelligence

The Fraude Intelligence example shows Scrydon set up for a financial-services AML / fraud-detection analyst. It ties together managed tables, the typed ontology layer, and an agentic workflow — so the analyst can ask plain-language questions and get governed, typed, cited answers.

What it models

A regulator's view of:

• Transactions between counterparties.
• Customers (the persons / entities party to those transactions).
• Suspicious activity reports (SARs).
• Regulated entities and the regulators that supervise them.
• Risk assessments and supervisory actions.

The example ships a Fraude Intelligence ontology pack with pre-built bindings against the demo CSVs. Once installed, the analyst can ask:

• "Show me all transactions involving Acme Holdings that triggered a SAR in Q1."
• "Which regulators supervise the high-risk entities in Belgium?"
• "What's the trend in supervisory actions against fintechs?"

Download

The pack is a downloadable .scrydon-pack.tar.gz bundle — it is not preshipped with the platform.

• fraude-intelligence.scrydon-pack.tar.gz — latest.
• scrydon-pack-fraude-intelligence-1.0.0.scrydon-pack.tar.gz — version-pinned.

Install

1. Download the bundle above.
2. In your Scrydon deployment, open Settings → Platform → Packs.
3. Drag the .tar.gz onto the drop zone (or click to pick the file). The dialog shows the bundle's packageId, version, and ontology contributes summary.
4. Tick the unsigned-pack acknowledgement and click Upload. The pack appears in your org catalog as Fraude Intelligence v1.0.0.
5. In your workspace, open the Ontology marketplace and enable Fraude Intelligence for the active workspace-environment.
6. Upload the demo CSVs below as managed tables, then map the pack's bindings to them in /ontology → Bindings.

For the full walkthrough (schema inspection, binding column maps, graph verification, building an agentic triage workflow), see the step-by-step tutorial.

What's interesting about it

• Typed reasoning — the agent reasons about RegulatedEntity and Transaction, not about regulated_entities rows. Renaming a column doesn't break the workflow.
• Provenance everywhere — every agent answer cites the underlying rows and bindings.
• Multi-hop traversal — ask about regulators of entities involved in high-risk transactions; the agent traverses the links without you wiring the joins.
• Column-level masking — confidential fields (e.g. originatorName) are masked for members and visible to admins.

Sample data

Nine anonymised, cross-referenced CSVs in apps/docs/public/static/fraude-intelligence/:

• compliance_authorities.csv
• customer_due_diligence.csv
• persons.csv
• regulated_entities.csv
• risk_assessments.csv
• supervisory_actions.csv
• supervisory_authorities.csv
• suspicious_activity_reports.csv
• transactions.csv

Related

• Step-by-step tutorial — install, bind, verify, build an agentic workflow.
• Ontology — the typed layer.
• Ontology → Packs — how packs ship and install.
• Analytics → Classification & masking — column-level governance.
• Compliance → GDPR — applies for personal data in this scenario.