AI & Finance Automation

Let the system read the data first and tell you what changed.

A tailored workflow can consolidate routine inputs, while AI-assisted review surfaces anomalies, explains them in business language and proposes the next action.

Data consolidationAnomaly detectionPlain-language explanationsRecommended actions

AI detection feed

Signals reviewed this week

Sample data

Records processed

18,420

Automated

Anomalies flagged

7

This week

Manual hours saved

~11h

Per month

AI detected

Collection risk is rising even though revenue stays above plan.

Recommended action

Review the five largest overdue accounts before Week 10.

The problem

Teams lose analysis time preparing recurring reports and checking large sets of figures for exceptions.

Management can see

  • Which signals changed beyond agreed tolerances
  • Where an exception came from
  • Which suggested actions need human review

Example scenarios

  • Draft monthly variance commentary for review
  • Flag unusual cost or payment patterns
  • Prioritise exceptions for a management meeting

Detection layer

Source data → AI detection → business action.

Illustrative examples of the kind of signals the system surfaces.

Source data

Invoices & receivables

AI detected

Payment behaviour of 5 accounts slipped past 30 days

Recommended action

Start structured reminders and review credit terms

Source data

Sales & pricing

AI detected

Discount depth grew faster than volume on Line B

Recommended action

Cap discount authority and review the top 10 deals

Source data

Purchasing

AI detected

Unit purchase cost rose 3.1% without a price update

Recommended action

Model a 2% price adjustment on affected lines

Source data

Operations

AI detected

Delivery delays cluster in two regions

Recommended action

Review carrier SLA before peak season

Data signal

18,420 example records consolidated across sales, purchasing and cash data.

AI detected

Seven anomalies exceed the tolerance you defined.

Business action

Two require a decision this week; five are logged for monitoring.

Automation examples

The manual work that disappears.

Data consolidation

Available exports and spreadsheets combined into one structured dataset on an agreed schedule.

Reconciliation checks

Rules can flag mismatches between invoices, payments and orders for review.

Report distribution

The management pack is generated and delivered on a fixed day each month.

Payment reminders

Overdue invoices trigger a structured reminder sequence.

Threshold alerts

Cash, margin and overdue limits notify the right person immediately.

Commentary drafting

AI drafts the variance explanation for your review, not your typing.

How it runs

AI-assisted preparation with a human decision at the end.

1

Ingest

Data is collected from your existing systems on a schedule.

2

Clean

Duplicates, gaps and mismatches are resolved by rules.

3

Detect

AI compares against plan, history and your tolerances.

4

Decide

You get a short list of actions — the decision stays yours.

Implementation approach

The system can be built around the data and tools your business already uses. Data sources and integration options are assessed during implementation; Excel, CSV files, spreadsheets and existing business systems can be considered depending on the project.

Ready to see this running on your numbers?

A practical workflow can reduce routine preparation while keeping decisions under human control.