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Challenge

Build a Trade Finance Letter of Credit Inconsistency Inspector

Inspect trade finance documents including bills of lading, commercial invoices, and letters of credit for counterparty discrepancies. Collate address mismatches and missing ports into an investigation dossier.

CybersecurityHosted by Vera
Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Inspect trade finance documents including bills of lading, commercial invoices, and letters of credit for counterparty discrepancies. Collate address mismatches and missing ports into an investigation dossier.

Trade finance operations auditor

Ingest simplified normalized trade finance packets containing LC rules, commercial invoices, and shipping bills.

Extract counterparties, cargo descriptions, declared weights, and loading/unloading port codes.

Reconcile shipping party names against GLEIF legal entity names to verify counterparty authority.

Flag cross-document discrepancies such as destination port drift or altered cargo descriptions in an audit log.

Side-by-side document comparison split-screen highlighting discrepancies between LC stipulations and shipping manifests.

Construct 35 synthetic trade finance dossier JSON objects featuring consistent transactions and intentional discrepancies. Starter examples below are synthetic. They are not downloaded records.

GLEIF API Verifies legal names and jurisdiction registration of trade counterparties against simulated GLEIF reference records.

pydantic-ai, pydantic, fastapi. Pydantic AI structures multi-document validation logic and generates structured discrepancy reports. Local or hosted LLM API capable of document reasoning with structured output validation.

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.

Detects discrepancies between letter of credit stated destination and invoice destination.

Verifies freight forwarder entity status against simulated GLEIF legal entity records.

Compiles an exportable JSON trade reconciliation summary for trade compliance analysts.

```json [ { "input": { "lc_id": "LC-100", "lc_applicant": "Oceanic Traders", "invoice_buyer": "Oceanic Traders", "port_of_loading": "SGSIN", "port_of_discharge": "NLRTM" }, "expected_output": { "discrepancies": [], "status": "COMPLIANT" } }, { "input": { "lc_id": "LC-101", "lc_applicant": "Oceanic Traders", "invoice_buyer": "Oceanic Logistics Sub", "port_of_loading": "SGSIN", "port_of_discharge": "USNYC" }, "expected_output": { "discrepancies": [ "Port of discharge mismatch: NLRTM expected vs USNYC provided", "Counterparty name discrepancy between LC applicant and invoice buyer" ], "status": "DISCREPANT" } } ] ```

Demonstration tool for document reconciliation; does not delay port shipments or halt financial letters of credit. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Demonstration tool for document reconciliation; does not delay port shipments or halt financial letters of credit. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Detects discrepancies between letter of credit stated destination and invoice destination. Verifies freight forwarder entity status against simulated GLEIF legal entity records. Compiles an exportable JSON trade reconciliation summary for trade compliance analysts.

Submission format

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

Datasets

Challenge datasets

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

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

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

Complete workflow

Ingest simplified normalized trade finance packets containing LC rules, commercial invoices, and shipping bills. Extract counterparties, cargo descriptions, declared weights, and loading/unloading port codes. Reconcile shipping party names against GLEIF legal entity names to verify counterparty authority. Flag cross-document discrepancies such as destination port drift or altered cargo descriptions in an audit log.

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

Detects discrepancies between letter of credit stated destination and invoice destination. Verifies freight forwarder entity status against simulated GLEIF legal entity records. Compiles an exportable JSON trade reconciliation summary for trade compliance analysts.

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

Side-by-side document comparison split-screen highlighting discrepancies between LC stipulations and shipping manifests.

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

Demonstration tool for document reconciliation; does not delay port shipments or halt financial letters of credit.

Binary check

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

Learning goals

What you should walk away with

  • Ingest simplified normalized trade finance packets containing LC rules, commercial invoices, and shipping bills.

  • Extract counterparties, cargo descriptions, declared weights, and loading/unloading port codes.

  • Reconcile shipping party names against GLEIF legal entity names to verify counterparty authority.

  • Flag cross-document discrepancies such as destination port drift or altered cargo descriptions in an audit log.

Resources and assets

Reference links and supporting material

Dataset notes

Construct 35 synthetic trade finance dossier JSON objects featuring consistent transactions and intentional discrepancies. 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.

Evaluation checks
  • Ingest simplified normalized trade finance packets containing LC rules, commercial invoices, and shipping b...
  • Detects discrepancies between letter of credit stated destination and invoice destination. Verifies freight...
  • Side-by-side document comparison split-screen highlighting discrepancies between LC stipulations and shippi...
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 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

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 Trade Finance Letter of Credit Inconsistency Inspector