Build a Cross-Border Counterparty Data Correction Queue
Inspect inconsistent cross-border counterparty records against official LEI reference datasets using the GLEIF API. Detect address drift and BIC mismatches to format structured remediation proposals for payment operations analysts.
What you are building
The core problem, expected build, and operating context for this challenge.
Inspect inconsistent cross-border counterparty records against official LEI reference datasets using the GLEIF API. Detect address drift and BIC mismatches to format structured remediation proposals for payment operations analysts.
Cross-Border Payment Operations Analyst
Ingest simplified normalized payment records containing counterparty names, destination BICs, and declared addresses.
Query GLEIF API search endpoints to match counterparties by legal name, BIC code, and registration country.
Compare counterparty postal codes, registered cities, and legal status to detect corporate changes or drift.
Synthesize structured remediation change tickets specifying original, suggested, and verified corporate registry attributes.
A web queue presenting side-by-side original payment counterparty records against official GLEIF corporate data with inline edit approvals.
Generate synthetic payment records paired with mocked GLEIF API response payloads simulating public Golden Copy entity endpoints. Starter examples below are synthetic. They are not downloaded records.
GLEIF API GLEIF API search endpoint features and field structures (BIC mappings, address structures, and parent-child relationship filters).
pydantic-ai, fastapi, httpx. Enforces type-safe schema validation on incoming payment fields and outgoing remediation ticket structures. Fast local instruction-tuned LLM or cloud API supporting strict JSON schema generation.
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.
Correctly flags BIC and address discrepancies between payment records and registry data.
Generates typed correction payloads matching the verification review schema.
Simulates outbound remediation patch payloads without mutating external databases directly.
```json [ { "input": { "payment_id": "PMT-88210", "counterparty_name": "Nordic Industrial Solutions AB", "bic": "NDEASESS", "address": "Kungsgatan 12, Stockholm, SE" }, "expected_output": { "payment_id": "PMT-88210", "lei": "5493006MHB84DD0ZWV18", "match_confidence": 0.95, "discrepancies": [ { "field": "address.postal_code", "payment_val": "Kungsgatan 12", "registry_val": "Kungsgatan 12, 111 35 Stockholm" } ], "remediation_status": "PROPOSED_UPDATE" } }, { "input": { "payment_id": "PMT-99301", "counterparty_name": "Unknown Global Trading", "bic": "INVALIDBIC", "address": "Unknown" }, "expected_output": { "payment_id": "PMT-99301", "lei": null, "match_confidence": 0, "discrepancies": [ { "field": "bic", "payment_val": "INVALIDBIC", "registry_val": null } ], "remediation_status": "UNRESOLVED_ESCALATION" } } ] ```
Ensures no live SWIFT or payment network updates are executed; all outputs are mock staging proposals. Use mock external writes. Require human review before any real deployment.
Participation guide
Ensures no live SWIFT or payment network updates are executed; all outputs are mock staging proposals. Use mock external writes and human review.
How work is evaluated
Correctly flags BIC and address discrepancies between payment records and registry data. Generates typed correction payloads matching the verification review schema. Simulates outbound remediation patch payloads without mutating external databases directly.
Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Complete workflow
Ingest simplified normalized payment records containing counterparty names, destination BICs, and declared addresses. Query GLEIF API search endpoints to match counterparties by legal name, BIC code, and registration country. Compare counterparty postal codes, registered cities, and legal status to detect corporate changes or drift. Synthesize structured remediation change tickets specifying original, suggested, and verified corporate registry attributes.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Correctness and evidence
Correctly flags BIC and address discrepancies between payment records and registry data. Generates typed correction payloads matching the verification review schema. Simulates outbound remediation patch payloads without mutating external databases directly.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Review interface
A web queue presenting side-by-side original payment counterparty records against official GLEIF corporate data with inline edit approvals.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Safe execution
Ensures no live SWIFT or payment network updates are executed; all outputs are mock staging proposals.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Ingest simplified normalized payment records containing counterparty names, destination BICs, and declared addresses.
Query GLEIF API search endpoints to match counterparties by legal name, BIC code, and registration country.
Compare counterparty postal codes, registered cities, and legal status to detect corporate changes or drift.
Synthesize structured remediation change tickets specifying original, suggested, and verified corporate registry attributes.
Reference links and supporting material
Generate synthetic payment records paired with mocked GLEIF API response payloads simulating public Golden Copy entity endpoints. Supplied starter examples are synthetic, not source extracts.
Challenge setup
Configuration summary for this challenge.
Challenge input
Input details are not configured.
Pydantic AI
Typed Python agent framework.
Evaluated output
No output checks are configured.
- Ingest simplified normalized payment records containing counterparty names, destination BICs, and declared...
- Correctly flags BIC and address discrepancies between payment records and registry data. Generates typed co...
- A web queue presenting side-by-side original payment counterparty records against official GLEIF corporate...
- 2 public reference cases
- 2 configured tools
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Pydantic AI · Optional
- Pydantic AI · Optional
Evaluation contract
- Complete workflow · Weight 40
- Correctness and evidence · Weight 30
- Review interface · Weight 20
- Safe execution · Weight 10
Reference cases
- 2 public reference cases
Workflow tasks
No workflow tasks are configured.
Recipe freeze marker
No recipe freeze time is recorded.