Datasets:

RegalFire
/

GCC-InvoiceMath-Verified

license: mit
task_categories:
  - tabular-classification
  - text-classification
language:
  - ar
  - en
tags:
  - synthetic-data
  - gcc
  - invoices
  - ecommerce
  - erp
  - fintech
  - testing
  - data-quality
  - arabic
  - benchmark
pretty_name: GCC-InvoiceMath-Verified
size_categories:
  - n<1K

GCC-InvoiceMath-Verified

A deterministic synthetic benchmark for testing invoice arithmetic, numeric parsing, rounding, and claimed-total validation across six GCC currencies.

Overview

GCC-InvoiceMath-Verified contains 600 records / 300 causal pairs.

Each pair contains:

one mathematically correct invoice
one counterfactual invoice where exactly one claimed total differs by one minor currency unit

The dataset is intended for:

ERP QA
e-invoicing integration testing
financial software regression testing
structured-output validation
numerical reasoning evaluation
synthetic data benchmarking

This benchmark is not a tax-law compliance benchmark.

It does not certify compliance with ZATCA, UAE e-invoicing rules, PINT, UBL, or any national tax regulation.

Countries and currencies

Country	Currency	Records
Saudi Arabia	SAR	100
United Arab Emirates	AED	100
Qatar	QAR	100
Kuwait	KWD	100
Bahrain	BHD	100
Oman	OMR	100

SAR, AED and QAR use two benchmark fractional digits.

KWD, BHD and OMR use three benchmark fractional digits.

Scenario families

The benchmark contains 10 deterministic scenario families:

half_up_tie
line_vs_document_rounding
line_allowance
line_charge
prepayment
credit_note
large_amount_stress
zero_rate
mixed_rates
zero_due

Each family contains 60 records.

Numeric rendering

Four rendering styles are equally represented:

ASCII
grouped ASCII
Arabic-Indic digits
grouped Arabic-Indic digits

Each style contains 150 records.

This makes it possible to test parsers handling both Western and Arabic-Indic numeric representations.

Dataset structure

Important fields include:

{
  "id": "...",
  "pair_id": "...",
  "split": "train",
  "country": "SA",
  "family": "half_up_tie",
  "contract_id": "gcc-invoice-math-1.0",
  "invoice": {
    "currency": "SAR",
    "document_type": "invoice",
    "numeric_style": "arabic_indic",
    "lines": [],
    "prepaid": "...",
    "claimed_totals": {}
  },
  "expected": {
    "valid": true,
    "totals": {},
    "error_fields": []
  },
  "mutation": null,
  "fingerprint_sha256": "..."
}
Ground truth
Ground truth is generated procedurally using Python Decimal arithmetic with explicit ROUND_HALF_UP behavior.
Validation is performed independently using fractions.Fraction.
The validator does not import generator code.
This provides two different arithmetic implementations:
- generator oracle: Decimal
- validation oracle: rational arithmetic
Validation results
- Records generated: 600
- Records validated: 600
- Pass rate: 100%
- Valid examples: 300
- Invalid examples: 300
- Unique causal pairs: 300
The release also checks:
- JSON Schema compliance
- unique IDs
- unique semantic source invoices
- fingerprints
- country/currency consistency
- pair integrity
- train/validation/test pair isolation
- exactly one causal mutation per invalid record
- exactly one minor-unit difference
- generator/validator oracle agreement
Splits
- train: 360
- validation: 120
- test: 120
Positive and negative members of a causal pair are always kept in the same split.
Synthetic provenance
All records are procedurally generated.
- no real invoices
- no PII
- no copied commercial records
- no real tax identifiers
- no customer transaction data
External sources were used only for background research and currency-precision verification.
Important limitations
This dataset does not cover:
- XML / UBL validation
- digital signatures
- QR codes
- live tax submission
- regulatory certification
- real tax IDs
- OCR images
- exchange rates
- payment reconciliation
- jurisdiction-specific VAT interpretation
Tax rates used in the benchmark are synthetic testing parameters.
Intended use
Recommended uses:
- unit tests
- regression tests
- ERP adapter QA
- invoice parser evaluation
- financial-agent evaluation
- data-quality pipelines
- numeric-reasoning benchmarks
Commercial customization
Need a private benchmark matching your ERP, invoice schema, accounting rules, API, or failure modes?
Custom synthetic datasets can be generated with:
- client-specific field mappings
- custom currencies
- custom rounding behavior
- additional failure modes
- private held-out regression suites
- custom JSON / CSV / Parquet / SQL formats
- validation scripts
- QA reports
The commercial value is in adapting the benchmark to a real system, not simply generating more generic rows.
Reproducibility
The generator is deterministic and seed-based.
Default seed:
20260930
See generator.py and validator.py.
License
MIT License for the original benchmark code and synthetic fixtures.
See LICENSE.

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