Implementing Triple Entry Accounting as an Audit Tool—An Extension to Modern Accounting Systems
Bibliographic record
Abstract
Despite the technological leaps modern accounting systems have brought about, fraud is still prevalent. This necessitates spending time and money on audits. Connecting modern accounting ledgers to a public blockchain would make accounting records easily verifiable, aiding the audit evidence gathering and analytics process. Continuous audits will be made viable by readily available audit evidence from the public blockchain. The proposed audit solution creates verifiable “Financial Fingerprints” (signed indexes) for every accounting entry. These Financial Fingerprints are then published to a certain range of addresses on the public blockchain. The objective is to help accounting ledgers be structured in a Triple Entry Accounting format. The third entry can be defined as publishing a notarized index of each accounting entry to the public blockchain. The crux of Triple Entry Accounting is atomicity, meaning there can only be one book. Trying to present an alternative set of books would still be possible, but it would be automatically detected. The method used to achieve the objective can be presented as two value propositions. The first value proposition of Triple Entry Accounting is to define a provable official true set of books. This is important because it is a door into the market without being dependent on network effects. The second value proposition of Triple Entry Accounting is to break down the data silos by using the blockchain as a shared ledger and thus help auditors immensely in their tedious work to gather sufficient and appropriate audit evidence. Because of value proposition number one, Triple Entry Accounting presents standalone value even before the network effect begins. The result of this is an open standardized protocol for implementing Triple Entry Accounting as an extension to modern accounting systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".