Assigning accounting attributes such as Suppliers, Expense Accounts, VAT Rates, Cost Centres, Cost Carriers and other dimensions to transactions, invoices and reimbursements follows different rules, patterns and practices in every organisation.
AI Pre-Accounting automatically pre-codes the majority of your transactions, invoices — and business expenses in the Reimbursements module — for you.
Here is how we do it:
Learning from your data
AI Pre-Accounting reviews the accounting attributes you assign when exporting expenses. Those historical mappings are the foundation it uses to learn how your organisation codes spend.
Building an AI model for your organisation 🤖
Once you've exported your first 150 transactions, 25 invoices or 25 business expenses (Reimbursements), we automatically train an organisation- and spend-type-specific AI model using your data. The model analyses how Expense Accounts, VAT rates and other dimensions were assigned previously and uses those patterns to predict the correct accounting details for future items.
Prediction with AI 🤖
For all future transactions, invoices and reimbursements the model attempts to predict VAT, Expense Account and other dimensions. Predictions are probability-based: when confidence for a single value falls below the minimum threshold, the field is left empty rather than guessed, reducing the risk of incorrect matches.
When there are multiple higher-probability options, AI Pre-Accounting surfaces up to five suggestions in the dropdowns, pinned to the top so accountants can quickly choose the correct value without browsing the full list. If no suggestion meets the minimum confidence threshold, the dropdown will not show suggestions and fields remain empty for manual input.
Our internal data shows AI Pre-Accounting assigns correct accounting attributes more frequently than static rule sets, so we recommend enabling the "AI Pre-Accounting overrides rules" option in Settings so the top prediction(s) take priority. The default is rule-first, so rules remain authoritative until you opt in.
Self-learning with AI 🤖:
The Agent continuously improves through a feedback loop. If it makes an incorrect prediction, you can correct it and the Agent will learn from that correction. Each time you export transactions, invoices or reimbursements, we (re)build an updated model that incorporates your previous corrections and the newest exported data.
If you delete a specific Expense Account, VAT rate or other value, we rebuild the model so those values are no longer predicted.
Which spend types are supported and which fields can be predicted
Card transactions
Fields that can be predicted: Expense Account, VAT Rate, Cost Center, Cost Carrier, Custom Dimensions (if configured), Supplier
Invoices
Fields that can be predicted: Expense Account, VAT Rate, Cost Center, Cost Carrier, Custom Dimensions (if configured), Booking Text
Reimbursements (business expenses only)
Fields that can be predicted: Expense Account, VAT Rate, Cost Center, Cost Carrier, Custom Dimensions (if configured)
Low-confidence predictions
When AI Pre-Accounting is less certain about a prediction, the field is highlighted in yellow. This means the value has been pre-filled but falls below the high-confidence threshold — you should review it before confirming.
Yellow predictions behave like any other pre-filled value: you can accept them as-is, edit them, or clear them. If you correct a yellow prediction, AI Pre-Accounting learns from that correction and will improve over time.
If confidence is too low even for a yellow prediction, the field is left empty and suggestions are shown in the dropdown instead.
When the Agent can’t predict your data entries (and what to do)
New or rarely used Expense Accounts or VAT rates
AI Pre-Accounting needs examples to learn. If a value appears very rarely in your data (for example fewer than ~10 transactions), it's unlikely to be predicted reliably — and it may be ignored.
Identical items linked to different accounts
If identical transactions or invoices are routinely coded to different accounts (e.g. the same supplier is sometimes charged to different Cost Centres), AI Pre-Accounting will struggle to infer a clear pattern and will tend to pick the most common value.
Note: AI Pre-Accounting models are (re-)trained using data that has been exported to your accounting system. Assigning accounting attributes without exporting the transaction, invoice or reimbursement will not improve future predictions. To get the best results, export consistently and keep your exported data accurate.