Financial data automation is the removal of manual handling from the movement, preparation and reconciliation of financial data across the finance function. It pulls balances and transactions from banks, payment service providers and internal systems, standardises formats, applies transformation logic and delivers the result on a chosen schedule. The scope extends across treasury, payments, reconciliation, safeguarding and reporting. The defining characteristic is that data arrives prepared rather than being collected, so the finance team starts at analysis rather than at extraction, transformation and loading.

Accounting automation is the use of software to carry out accounting work that was previously done by hand. Most definitions focus on the ledger itself: transaction matching, journal preparation, accruals, intercompany eliminations and the assembly of audit evidence. The step before that gets less attention and determines whether the rest works. Accounting automation operates on data that has to arrive first, from banks, payment service providers, ERPs and internal systems, each in its own format. Where that ingestion is automated, matching runs on complete data from the start. Where it is not, the preparation work stays manual, time consuming and error prone, diminishing the returns of automated matching.

Automation and AI do different jobs but can compliment one another. Automation runs the defined work at volume. AI handles what falls outside the definition: the unmatched transaction, the remittance advice that arrives as a PDF, the fee deduction that changes format each month. AI also changes how the work gets built, since describing a process in plain English can produce the workflow itself rather than a specification for someone else to configure. The same goes for visibility, dashboards and reporting, a single request customises what can be seen.

It works in four stages. You connect the source, which for Aurum means more than 6,000 integrations spanning banks, payment service providers, ERPs and effectively any system with an API or a file drop. Authentication is handled by the platform through 14 built-in methods, so the connection complexity sits with us. You then shape the data using more than 120 built-in functions, configure the full workflow in the interface without writing code, and put it on a schedule. From that point the data arrives clean, transformed and ready to use before the team logs on.

Data preparation stops being a morning task, because scheduled workflows complete before the team arrives. Dependency on individual scripts and spreadsheets reduces, which matters when the person who built them is on leave. Capacity moves from gathering data to interpreting it, which is what finance teams are hired to do.