Finance
Credit risk, delinquency and automated close. The finance team produces the most spreadsheets and should produce the fewest.
Talk to a specialistThe first working days of the month should not be spent on copy and paste.
In almost every company the close starts the same way: export from one system, reconcile with another, adjust what did not match and assemble the report. It takes days, happens every month, and consumes precisely the people who should be interpreting the result.
Meanwhile credit decisions keep being made by fixed rules and personal history, and collections treat every late payer the same without distinguishing who has a real chance of paying from who does not.
The questions we answer
- How many days does the close consume, and how much of that is manual work?
- Which customer represents real default risk, and how far ahead can we know?
- Where should collections concentrate effort to recover more with the same team?
- What is the margin per business unit after correct allocation?
- Does the projected cash flow match the actual? If not, where does it miss?
The indicators of this industry
The ones that show up in most projects in this industry. The final set is always agreed with you an indicator nobody uses is dead weight on a dashboard.
- Close. Days to close the month, manual hours per cycle, number of post-close adjustments, entries reconciled automatically.
- Credit and collections. Delinquency by ageing band, days sales outstanding, receivables ageing, recovery rate by collection sequence, risk score per customer, provision for doubtful debt.
- Result. Margin per business unit after allocation, EBITDA by line, expense over revenue, fixed versus variable cost, return per cost centre.
- Cash. Projected versus actual cash flow, working capital, cash conversion cycle, payables and receivables days combined, cash requirement by period.
The solutions we use most here
- Integration & AI Automation. The reconciliation and consolidation that today block the first working days.
- Data Analytics. Credit risk scoring and collection prioritisation by chance of recovery.
- Data Governance. Audit trail and least access a requirement of the sector, not an optional extra.
How we run it
We start by measuring the current close: how many hours, how many people and at which stages. That survey defines what to automate first, and the return usually shows up the following month. Risk models come later, always compared with the rules you use today if they do not beat them, they do not go into production.
How we measure results
The most frequent indicators are reduction in days to close, manual hours given back to the team, collection recovery rate and risk model accuracy compared with the previous rules.
We now have a far more robust, fast and visual view of each management area’s results.
Sales director national fuel distributor
About this industry
Doesn’t automating the close increase the risk of error?
The bigger risk sits in the current manual process, where a typing error only surfaces when someone happens to notice. Every automated routine comes with checking rules and alerts: if something arrives out of pattern, someone is notified before the number circulates.
We handle sensitive customer data. How is that treated?
It is treated as a requirement from the design stage: least access by role, audit trail and mapping of where personal data sits. That is the Data Governance layer, and in finance it is not optional.
Does a risk model replace our credit analysis?
It does not replace the analyst; it prioritises their work. The model classifies the volume and points at where to look closely, and the decision on borderline cases stays human. We also deliver the measured margin of error, so you know how much to trust it.
Our ERP is old. Can it be integrated?
It can. Where an API exists we use the API; where it does not, we extract another way. An old ERP is common in finance and does not block the project it just feeds into the timeline.
How many days does your close consume?
Thirty minutes of conversation is usually enough to estimate how much of it can be automated.
We understand processes before recommending technology. Automation, data, artificial intelligence, and custom software for companies seeking efficiency, control, and scale.