Build a Financial Crime Investigation Workload Prioritization Dispatcher

Score and prioritize financial crime investigative queues using configurable fictional business rules. Map case attributes to investigator specialization and capacity to produce transparent operational rosters.

AML Investigation Team Lead

Ingest batches of pending fictional financial crime case files containing entity risk, value, and alert types.

Calculate deterministic priority scores using weighted factors for corporate exposure, cross-border tags, and statutory SLA limits.

Evaluate investigator roster metrics including active load, jurisdiction certifications, and current backlog.

Generate a balanced investigation dispatch roster with explicit routing rationales and escalation markers.

Investigative operations dispatch console showing backlog queues, investigator capacity gauges, and assignment logs.

Construct a synthetic JSON fixture of fictional alert records and investigator profiles. Starter examples below are synthetic. They are not downloaded records.

OpenSanctions data toolkit Employ FollowTheMoney entity property types (jurisdiction, amount, currency, entity schema) for uniform alert attributes.

langgraph, pydantic, fastapi. LangGraph manages stateful queue routing loops and conditional dispatcher nodes efficiently. Local or hosted LLM with tool-calling capabilities for routing decisions.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Calculates deterministic priority rank adhering to SLA deadline and multi-factor risk weighting.

Respects investigator caseload caps during automated assignment passes.

Provides clear reason codes explaining why a case was routed to a specific analyst.

Returns full priority queue JSON payload with valid ISO timestamps and routing keys.

```json [ { "input": { "cases": [ { "case_id": "CASE-901", "entity_type": "Company", "jurisdiction": "ky", "aggregate_amount": 750000, "days_to_sla": 2 }, { "case_id": "CASE-902", "entity_type": "Person", "jurisdiction": "us", "aggregate_amount": 12000, "days_to_sla": 14 } ], "investigators": [ { "id": "inv_1", "name": "Elena Vance", "active_cases": 2, "max_capacity": 5, "specialties": [ "offshore", "corporate" ] }, { "id": "inv_2", "name": "Marcus Reid", "active_cases": 4, "max_capacity": 5, "specialties": [ "retail", "structuring" ] } ] }, "expected_output": { "dispatches": [ { "case_id": "CASE-901", "assigned_to": "inv_1", "priority_tier": "critical", "score": 95, "routing_reason": "SLA expiring in 2 days and offshore corporate jurisdiction match." }, { "case_id": "CASE-902", "assigned_to": "inv_2", "priority_tier": "low", "score": 30, "routing_reason": "Low value domestic person alert within safe SLA threshold." } ], "unassigned_cases": [] } }, { "input": { "cases": [ { "case_id": "CASE-903", "entity_type": "Company", "jurisdiction": "pa", "aggregate_amount": 1500000, "days_to_sla": 1 } ], "investigators": [ { "id": "inv_3", "name": "Sarah Chen", "active_cases": 5, "max_capacity": 5, "specialties": [ "offshore" ] } ] }, "expected_output": { "dispatches": [], "unassigned_cases": [ { "case_id": "CASE-903", "priority_tier": "critical", "score": 98, "status": "queued_capacity_exceeded", "routing_reason": "Investigator capacity full; requires supervisor rebalancing." } ] } } ] ```

Utilizes purely synthetic scenarios with no real names, simulated routing only, and no automated regulatory reporting. Use mock external writes. Require human review before any real deployment.