Expense CategorizationAutomation
Let transactions arrive already categorised, tagged to the right team and checked against policy, so finance reviews outliers instead of every line.
Expense Categorization Automation: what the work involves
Expense lines arrive as cryptic merchant strings: a ride-hailing charge, a SaaS renewal, an airline booking, a line reading only POS 2281. Someone in finance must decide whether each belongs under travel, software or entertainment, which project to charge, and whether the claim obeys policy. Employees wait weeks for reimbursement and managers rubber-stamp reports because there is no time to look closely.
DevKey builds a classifier tuned to your own chart of accounts and expense policy. Merchant text, amount, card holder, receipt contents and any note the employee wrote are combined to propose a category, department and project. Policy rules, such as per-meal limits or required receipts, are checked in code. Confident cases flow straight to the ledger as drafts; borderline ones go to an approver with the reason for the flag. Every correction trains the next suggestion.
Core features
Merchant normalisation
Messy descriptors are cleaned and matched to a merchant list, so a dozen spellings of the same vendor resolve to one entity with a known default category.
Context-aware categorisation
The same airline might be travel or client entertainment depending on the project and note, and the model weighs these signals alongside your past coding.
Policy checks in code
Spending caps, missing receipts, weekend charges and restricted merchants are tested with explicit rules that approvers can read and change.
Receipt matching
Photos or emailed receipts are read and paired with the card transaction, filling in tax amounts and itemisation when available.
Approver digest
Managers receive a short list of items needing attention, each with the flag reason, rather than a full report of every purchase.
Learning from corrections
When a finance officer recodes a line, the change updates merchant defaults and appears in a report of the rules the team is actually applying.
What we get right before launch
Policy is a business decision, not a model output
The language model suggests categories; it does not decide what is allowed. Limits and exceptions live in a configuration your finance head owns, and are applied the same way for everyone.
Employee privacy and fairness
Flagging spend patterns can feel like surveillance. We limit what is analysed, explain flags in plain terms, and keep a route for employees to respond before any consequence.
Category drift
New vendors and a revised chart of accounts quietly degrade accuracy. We track how often suggestions are overridden and prompt a rules review when that rate climbs.
Tools and technology
- OpenAI GPT
- Python
- scikit-learn
- FastAPI
- PostgreSQL
- Xero and QuickBooks APIs
- n8n
- Next.js
- Docker
Common questions, answered
How accurate is the categorisation?
It depends on your data. We test on past coded transactions, report where it agrees with your accountant and where it does not, and set the confidence level above which items skip review.
Can it work with our bank or card feed?
Usually, through bank feeds, card provider exports or accounting software connections. For local cards and wallets, we typically ingest CSV or statement files and normalise them first.
Will it approve or reject claims by itself?
It flags and recommends. By default, a manager or finance officer makes the decision on anything outside policy. Auto-approval for small, fully compliant claims can be enabled if you choose.
What about personal expenses on company cards?
Rules and merchant lists can highlight likely personal spend for review, but it is a prompt for a conversation, not proof. The tool is deliberately worded as a flag, not an accusation.
What influences the cost?
Transaction volume, the number of data sources, how elaborate the policy rules are, and whether receipts need reading. We estimate after looking at a sample of your expense data.
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Receipt Scanning Automation
Staff photograph a receipt on their phone; the system reads it, suggests a category and files the expense, so nobody keeps a drawer of crumpled paper.
Bank Reconciliation Automation
Match thousands of statement lines to ledger entries by rule and by fuzzy logic, leaving your team a short list of genuine differences to investigate.
Ready to start your Expense Categorization Automation project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.