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:

1. `half_up_tie`
2. `line_vs_document_rounding`
3. `line_allowance`
4. `line_charge`
5. `prepayment`
6. `credit_note`
7. `large_amount_stress`
8. `zero_rate`
9. `mixed_rates`
10. `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:

```json

{

  "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.


---

## RegalFire — Custom / Private Dataset Work

RegalFire builds custom AI datasets, evaluation sets and data pipelines for:

- AI agents
- computer-use systems
- multimodal models
- world models
- RAG systems
- code agents
- enterprise AI

Available services include:

- synthetic data generation
- private evaluation datasets
- agent trajectories
- failure / recovery datasets
- multimodal RGB / segmentation / state-action data
- web data acquisition
- cleaning and deduplication
- structured dataset packaging
- continuous dataset production

For custom or private work:

**Email: ootiris@gmail.com**

Hugging Face: **RegalFire**