{"id":28947,"date":"2026-07-24T08:46:00","date_gmt":"2026-07-24T08:46:00","guid":{"rendered":"https:\/\/bilnex.io\/ai-konteerimine\/"},"modified":"2026-07-24T08:46:00","modified_gmt":"2026-07-24T08:46:00","slug":"ai-accounting-2","status":"publish","type":"page","link":"https:\/\/bilnex.io\/en\/ai-accounting-2\/","title":{"rendered":"AI accounting: how does it work and how accurate will it be?"},"content":{"rendered":"
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AI accounting works in three steps. First, the machine reads the structural data from the invoice: supplier, lines, amounts, VAT. Then it offers an accounting based on your own history \u2013 invoices from the same supplier have previously gone to the same account, expenses with similar line text to the same place. Third, the degree of certainty decides what happens next: a high-certainty entry can be made automatically, a low-certainty offer comes to you with a choice. This is not magic, but a pattern: the system does the same thing as an experienced accountant who knows your chart of accounts \u2013 only tirelessly and for every invoice.<\/p>\n

How accurate is it really?<\/h2>\n

Honest answer: it depends on your history, and anyone who promises a specific percentage without seeing your data is offering an average, not you. The mechanics are predictable, though. Repeat suppliers (rent, communications, software licenses) are caught quickly by the system because the pattern is clear. New suppliers and atypical purchases are initially caught by the human, and each correction you make teaches the model. In practice, this means that accuracy is not a property of the moment of purchase, but a growth curve: in the first month you review a lot, in the third month less, and with a stable purchasing pattern, the review becomes sample-based. That about half of accountants already use AI<\/a>, this curve has been passed through thousands of offices.<\/p>\n

How are errors caught?<\/h2>\n

With three mechanisms, and they are more important than the accuracy percentage. Confidence threshold: transactions that the system is not sure about do not arise silently, but come to you for decision. Exception highlighting: an amount that differs from the supplier's usual, or an account that has not been used with that supplier before, gets a flag. And feedback on corrections: when you change the proposed posting, you are not correcting a single transaction, but teaching the rule. Ask three things of each solution: can the confidence threshold be set yourself, are exceptions in a separate view, and do the corrections teach the model - if any of the answers are no, you are looking at a demo, not a tool.<\/p>\n

When to trust accounting?<\/h2>\n

Then, when the numbers say so: watch for a few months to see if a large portion of the quotes pass through unchanged. If recurring vendor entries are correct for weeks without change, raise their threshold to automatic and keep manual review for new and large ones. Posting is the first step in the bigger picture: how does it bank comparison<\/a> and goes with the rest of the routine, shows automation roadmap<\/a>.<\/p>\n<\/div>\n<\/div>\n<\/article>\n