Refund Fraud? - Real-Time Solution! Digital Security Borrowed from the VAT (Brazil, Quebec, & Belgium)
Bibliographic record
Abstract
This article provides support for a proposal to eliminate refund fraud in the U.S. by turning Forms W-2, and 1099 into self-certified/ self-authenticated tax documents. The proposal suggests that a “digital signature” of these documents should be taken after they are completed. The signature should then be made part of the final document. This proposal was initially advanced in Refund Fraud? Real-Time Solution! The underlying premise of that article was that the US could dramatically reduce, if not eliminate, refund fraud if it borrowing digital security techniques from the VAT. The article did not however, explain or expand upon these techniques from within the VATs where they were developed. This article takes up the VAT side of that analysis. VAT frauds frequently manipulate documents for gain, and VAT jurisdictions have spent a considerable amount of time and energy devising effective and efficient methods for determining if a document is legitimate (or original). The strong suggestion is that the IRS should look to the VAT to solve refund fraud, because even though the tax is different, the administrative problem is the same. The refund fraud problem is essentially document verification problem. The VAT is very good at document verification. The IRS can learn from the VAT. This paper looks at three VAT jurisdictions, Brazil, Quebec and Belgium, and explains how they use technology to solve document authentication problems. In each case a tax fraud is facilitated by false documentation, and the administrative response is to use technology to certify the documents and stop the fraud. In Brazil the fraud arises in the context of internal cross-border B2B transactions. In Quebec and Belgium the fraud is skimming profits from B2C cash and debit/credit card transactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".