This content originally appeared on HackerNoon and was authored by Agustin V. Startari
AI-powered systems are misclassifying corporate expenses, not because they lack data, but because they misread grammar. What looks like a technical glitch reveals a deeper structural bias at the heart of automation.
What does grammar have to do with accounting? More than you think. In corporate and government ERP systems, every expense description is read by an AI model that assigns it to a general ledger category. This happens automatically, often without human oversight.
Now consider two phrases: \n "Reimbursement for coordination of lodging and transportation services"vs. \n "The office coordinated lodging and arranged transport."
They mean the same. But one is far more likely to confuse the AI.
Why? Because of syntax. Complex grammatical structures (like nominalizations or nested clauses) lead models to make the wrong call. The result?
Misclassification, accounting errors, and potentially audit failure.
The finding: AI fails not because it misunderstands—but because it obeys too literally \ A recent study shows that these systems don’t fail due to lack of training data.
They fail because they follow the form, not the meaning.
Transformer models like FinBERT rely heavily on the grammatical shape of sentences, often more than on their semantic content. Sentences with high syntactic density are statistically more likely to be misclassified.
So what’s the risk?
- Misclassified travel expenses
- Reimbursements logged under the wrong cost center
- Audits that don’t reconcile
- Regulatory violations
- Financial misstatements
All triggered by a sentence that "sounded too professional."
The fix: Rewrite to reduce risk
The paper proposes a structured rewriting method called the fair-syntax transformation. \n
It reformats expense descriptions into a simplified Subject-Verb-Object (SVO) structure, stripping out misleading grammatical forms.
Instead of: \n "Coordination of transport and accommodation services for vendor engagement"
Use: "The team booked transport and arranged accommodation."
This intervention alone reduced classification errors by 15%.
It improved code accuracy and reduced false positives.
The key insight? AI behaves better when we use simpler grammar.
What this says about AI in finance
AI systems don’t interpret, they execute.
Their logic is not semantic but syntactic.
That means that a well-written sentence can be more dangerous than a numerical error.
Who should care?
- CFOs, controllers, audit leads
- NLP engineers building ERP integrations
- Regulatory bodies developing audit-compliance standards
- Any enterprise automating expense workflows
- Anyone who’s ever submitted a reimbursement form with too much jargon
The uncomfortable conclusion:
Grammar is not neutral.
In automated systems, it governs decisions.
When syntax misaligns with accounting intent, the system doesn’t correct—it complies.
Author of the study: Agustin V. Startari \n [DOI (Zenodo): https://doi.org/10.5281/zenodo.16322760] \n [SSRN Author ID: https://papers.ssrn.com/sol3/cfdev/AbsByAuth.cfm?perid=7639915] \n [Website: https://www.agustinvstartari.com/] \n [ResearcherID: K-5792-2016 | ORCID: 0009-0001-4714-6539]
Ethos
I do not use artificial intelligence to write what I don’t know. I use it to challenge what I do. I write to reclaim the voice in an age of automated neutrality. My work is not outsourced. It is authored. \n — Agustin V. Startari
This content originally appeared on HackerNoon and was authored by Agustin V. Startari
Agustin V. Startari | Sciencx (2025-07-23T05:35:17+00:00) The Hidden Risk in ERP Automation: How One Bad Sentence Can Cost Your Company Millions. Retrieved from https://www.scien.cx/2025/07/23/the-hidden-risk-in-erp-automation-how-one-bad-sentence-can-cost-your-company-millions/
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