Build a Multi-Reviewer Watchlist Disposition Discrepancy Queue

Ingest screening adjudications from multiple compliance analysts to identify systematic review discrepancies. Flag out-of-policy determinations and highlight missing source evidence for supervisory audit.

Sanctions Audit Supervisor

Collect multi-analyst disposition records linked to FollowTheMoney entity screening candidates.

Compare adjudications against pre-defined screening policy rules to detect unauthorized false-positive clearances.

Verify if screening reviewers consulted the primary source lineage URL before recording dispositions.

Produce an aggregated discrepancy review queue highlighting rogue analyst patterns and unsubstantiated decisions.

Supervisor review cockpit displaying analyst disagreement heatmaps, policy deviation lists, and case escalation controls.

Synthesize a dataset of multi-analyst review logs referencing FollowTheMoney entity schemas. Starter examples below are synthetic. They are not downloaded records.

OpenSanctions data toolkit Adopt FollowTheMoney entity model properties and yente matching endpoint output structures.

crewai, pydantic, fastapi. crewAI coordinates independent auditor and policy-checker agent roles to cross-examine disposition logs. Hosted or local model capable of multi-agent debate and JSON structured extraction.

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.

Identifies 100% of cases where an analyst cleared a high-score candidate without providing mandatory comments.

Flags disposition variance where two analysts reached opposite conclusions on identical entity attributes.

Generates a structured supervisor remediation task for each flagged variance.

Produces valid discrepancy queue objects adhering to the audit schema.

```json [ { "input": { "review_event_id": "REV-3001", "entity": { "id": "ftm-66", "schema": "Vessel", "properties": { "name": [ "Oceanic Star" ], "flag": [ "pa" ] } }, "adjudications": [ { "analyst_id": "usr_9", "decision": "clear", "evidence_verified": false, "notes": "No match on IMO." }, { "analyst_id": "usr_4", "decision": "escalate", "evidence_verified": true, "notes": "IMO match verified in maritime appendix." } ] }, "expected_output": { "review_event_id": "REV-3001", "policy_violation": true, "discrepancy_score": 0.85, "supervisor_action": "require_re_review", "findings": "Reviewer usr_9 cleared vessel without verifying primary source appendix; contradicts usr_4 escalation." } }, { "input": { "review_event_id": "REV-3002", "entity": { "id": "ftm-88", "schema": "Person", "properties": { "name": [ "Alice Walker" ], "birthDate": [ "1975-04-12" ] } }, "adjudications": [ { "analyst_id": "usr_2", "decision": "clear", "evidence_verified": true, "notes": "DOB mismatch against subject record." }, { "analyst_id": "usr_7", "decision": "clear", "evidence_verified": true, "notes": "Different middle name and confirmed DOB variance." } ] }, "expected_output": { "review_event_id": "REV-3002", "policy_violation": false, "discrepancy_score": 0, "supervisor_action": "approve_closure", "findings": "Consensus clear disposition supported by verified DOB and name discrepancies." } } ] ```

Demonstration uses mock sanctions cases and analyst usernames; no actual AML or sanctions filings occur. Use mock external writes. Require human review before any real deployment.