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Alder Books Reconciliation: Bank reconciliation JSON

Bank reconciliation JSON for Alder Books Reconciliation. The same 24 matches as bank-reconciliation.csv with the reconciliation summary at the top: statement closing 10236.00, book closing 10236.00, nothing outstanding or unpresented and a reconciling difference of 0.00.

json

application/json

7.4 KB
Document Set
accounting
Industry
finance
Source Kit
accounting-reconciliation
Synthetic
true
As Of
2026-09-08
Matches
24

Binary json: no in-browser preview. Download it above to open in a compatible application.

Specifications

Document Set
accounting
Industry
finance
Source Kit
accounting-reconciliation
Synthetic
true
As Of
2026-09-08
Matches
24
Reconciling Difference
0.00
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Parse the reconciliation and check the summary against the match array.
Expected result
matches has 24 members whose differences are all 0.00; statementClosingBalance and bookClosingBalance are both 10236.00 and reconcilingDifference is 0.00.

What is a .json file?

JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.

How to use this file

Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.

How to use this file for testing

“Alder Books Reconciliation: Bank reconciliation JSON” is a deterministic Novus Examples fixture for Data import. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.

Documented properties for this file: UTF-8. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.

Data fixtures document their exact quirks (delimiters, encodings, null handling, schema, and row counts) in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.

Code examples

import json

with open("bank-reconciliation.json") as f:
    data = json.load(f)
print(type(data), len(data))

Generated by generation/industry_documents.py. Free for any use, no attribution required, license.