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Finance is, paradoxically, one of the richest areas in repetitive tasks and one of the last to adopt AI, out of excess caution. But caution is not paralysis. Here are seven processes where the return is clear and the risk is controllable.
1. Bank and invoice reconciliation
Matching movements, invoices and receipts is mechanical work that consumes days every month. AI does the automatic matching and flags only the exceptions for human review.
2. Supplier invoice processing
Extracting data from invoices in different formats, validating and posting them in the system stops being manual typing. The system reads the document, structures the information and prepares the entry.
3. Cash forecasting
Based on history and commitments, AI projects inflows and outflows and anticipates tight periods, giving the CFO time to act instead of react.
4. Collections and credit management
The system tracks deadlines, identifies at-risk customers and triggers reminders at the right moment, reducing days sales outstanding without straining the commercial relationship.
5. Management reporting
Instead of compiling spreadsheets every month, reports generate themselves from connected data, always up to date and ready for decision.
6. Anomaly and fraud detection
AI learns the normal spending pattern and flags what deviates, whether it is a duplicate payment or an unusual transaction, before the money leaves.
7. Internal request response
Questions like "what is the margin on this customer" stop requiring manual reconciliation. An assistant connected to the data answers in seconds.
Where to start
Do not start with all seven. Pick the one that steals the most hours from your team, automate that, and use the saving to fund the next one. Finance is the ideal place to prove ROI, because every gain is, by definition, measurable in euros. Before going ahead, it is worth estimating the running cost too: how to calculate the cost of an AI project based on tokens.
Writes about AI consulting for operations and processes: how to map and simplify processes, apply AI where it has the most impact and measure the gains.