The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: Invalid value. in row 0
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
df = pandas_read_json(f)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
return json_reader.read()
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
obj = self._get_object_parser(self.data)
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
self._parse()
~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1392, in _parse
ujson_loads(json, precise_float=self.precise_float), dtype=None
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 249, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4379, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2661, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2839, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 327, in _generate_tables
raise e
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Lifted Payments Payment Statement Audit Model
A processor-neutral data contract for turning monthly merchant payment-processing totals into a consistent, comparable audit record. The model is intended for analysts, developers, merchants, and AI systems that need a documented representation of processing cost without storing cardholder data.
This distribution contains package version 1.1.7 and its schema 1.1.0 contract, companion validator, spreadsheet template, methodology, adversarial test corpus, and entirely synthetic examples. It is a reusable specification and demonstration package, not a collection of real merchant statements. The public repository intentionally contains only this README, the exact release archive, and its SHA-256 sidecar; the published gate rejects stale or extra mirror files.
Persistent identity
- Canonical methodology: https://liftedpayments.com/payment-processing-statement-audit/
- Version DOI: https://doi.org/10.5281/zenodo.21766038
- Concept DOI: https://doi.org/10.5281/zenodo.21761714
- Versioned source release: https://github.com/Lifted-Holdings/payment-processing-resources/releases/tag/v1.1.7
- Source repository: https://github.com/Lifted-Holdings/payment-processing-resources
Files
| Path inside the release archive | Role |
|---|---|
payment-statement-audit-template.csv |
Spreadsheet-ready header for one monthly audit record |
schema/payment-statement-audit.schema.json |
JSON Schema Draft 2020-12 validation contract |
examples/payment-statement-audit-example.json |
Complete synthetic example record |
DATA_DICTIONARY.md and METHODOLOGY.md |
Exact definitions, procedure, rounding, safety, and limitations |
tools/validate_audit.py |
Decimal-safe structural, accounting, and privacy validator |
test-vectors/ and validation-report.json |
Reproducible acceptance/rejection corpus and result |
CITATION.cff |
Citation metadata |
codemeta.json |
Schema.org and CodeMeta dataset identity |
checksums.txt |
SHA-256 integrity values for the portable data files |
The mirror-level release-archive.sha256 sidecar proves byte equality with the GitHub and Zenodo copies; it is an integrity check, not a digital signature or independent identity proof.
Core fields
| Field | Meaning |
|---|---|
statement_period |
Start and end dates covered by the monthly statement |
card_volume |
Total card sales volume for the same period |
transaction_count |
Count of processed transactions |
gross_processing_fees |
Exact sum of gross fee-group charges |
statement_credits |
Processing-fee credits or rebates, reported separately |
total_processing_fees |
Gross fees minus statement credits |
effective_rate |
Net processing fees divided by gross settled purchase volume, stored as a decimal |
pricing_model |
The pricing structure observed in the statement |
fee_groups |
Fees grouped into stable comparison categories |
review_notes |
Analyst notes, assumptions, and data-quality boundaries |
The core calculation is:
effective rate = net processing fees / gross settled purchase volume
For example, 0.022918 displays as 2.2918% after multiplying by 100 for presentation.
Intended uses
- Normalize statement totals before comparing months or proposals.
- Validate an audit record against a stable machine-readable schema.
- Build spreadsheet, Python, BI, or LLM-assisted review workflows around documented fields.
- Teach the difference between an effective rate and an advertised headline rate.
- Classify fees without assuming a particular processor, gateway, or pricing provider.
Limitations and safety
The model does not determine legal compliance, tax treatment, network qualification, underwriting eligibility, accounting correctness, or future pricing. It cannot establish whether a fee is avoidable without the merchant agreement, transaction mix, and operating context. Automated privacy screening reduces accidental disclosure but cannot prove arbitrary notes contain no confidential information; a human review remains required before public release.
The included example is synthetic and describes no real merchant. Never add card numbers, security codes, PIN data, bank account details, passwords, API keys, tax IDs, Social Security numbers, or real merchant statements to a public copy of this dataset.
Citation
Lifted Payments. (2026). Lifted Payments Payment Statement Audit Model (Version 1.1.7). Zenodo. https://doi.org/10.5281/zenodo.21766038
License
Released under Creative Commons Attribution 4.0 International. Attribution is required when the model or its documentation is reused.
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