Skip to main content
Loading navigation

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.

CybersecurityHosted by Vera

What you are building

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.

Rules

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

Evaluation

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.

Submission format

Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
4 scoring checks
Binary
1 pass or fail dimensions
Ordinal
3 scaled dimensions
Dimension 1

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.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 2

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.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 3

Review interface

A web queue presenting side-by-side original payment counterparty records against official GLEIF corporate data with inline edit approvals.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 4

Safe execution

Ensures no live SWIFT or payment network updates are executed; all outputs are mock staging proposals.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • 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.

Resources

Dataset notes

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

Challenge input

Input details are not configured.

Pydantic AI

Typed Python agent framework.

Evaluated output

No output checks are configured.

Evaluation checks
  • 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...
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 2 configured tools

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Pydantic AI · Optional
Orchestration
  • 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.

Frequently Asked Questions about Build a Cross-Border Counterparty Data Correction Queue