Making the financial close understandable before automating it.

The close process depended on spreadsheets, financial systems, approvals, recurring exceptions and knowledge held by experienced employees. Before automating anything, Orgni made that whole operation understandable.

A fragmented operation

Every month, this mid-market finance team spent five gruelling days on close. The mechanics lived in spreadsheets and ledgers; the understanding lived in people. Which exceptions recur, who approves what, which corrections are routine and which are warnings - all of it was trapped in experienced employees' heads and reconstructed from scratch each month.

The team was capable. The problem was that the close, as an operation, was not understandable to anyone or anything outside the people who ran it. Automation attempted on top of that fragmentation would have been brittle.

Building the organisational model

We turned on financial operations in Orgni, plugged into the firm's ERP and banking data feeds. But the first step was not automation. It was representation: Orgni modelled the close as a live picture of responsibilities, dependencies, controls, deadlines and historical decisions.

• Responsibilities. Who owns each reconciliation, review and sign-off.
• Dependencies. Which inputs each step waits on, across ledgers, feeds and teams.
• Controls and rules. The thresholds, approvals and policies that govern the close.
• Decision history. How previous exceptions were investigated and resolved.

What became possible

Once the operation was understandable, reliable automation followed. Transactions are matched across ledgers and bank feeds automatically. Discrepancies are flagged with context: what changed, why it likely changed, and how similar cases were resolved before. Missing inputs and bottlenecks are identified before they stall the close.

The team reviews, approves and signs off - instead of rebuilding the close from scratch each month.

The numbers

Understanding that compounds

Month-end close now completes overnight, and accuracy has improved, because exceptions are caught systematically rather than by whoever happens to notice. But the close was not solved once: every resolved exception and decision feeds back into the model, so each close makes the next one more understandable.

The outcome was not another isolated automation. It was reusable organisational understanding that could support future teams, systems and agents.

"We used to dread the last week of every month. Now close just happens. The team is working on things that actually require their expertise."- CFO, financial services firm