Reconciliation exception investigation
Finance · Monthly reconciliation process
Validate further
The opportunity appears capable of creating meaningful capacity, cycle-time and control value, but the current evidence is not strong enough to justify technology selection or investment. The strongest pattern is process/matching improvement plus workflow, with selective automation and AI assistance for exception investigation.
Exception investigation consumes disproportionate effort
The team performs routine matching successfully, but unresolved items require manual investigation across several systems and supporting documents. The main constraint appears to be exception handling rather than basic matching.
Reduce investigation effort without weakening financial control
Shorten month-end effort, reduce backlog and improve evidence quality while retaining human accountability for material exceptions and financial approval.
€58k-€79k annual capacity value
Hypothetical basis: 3,200 annual investigation hours at €45 loaded hourly cost, with 40%-55% addressable through process, workflow and automation improvements.
Not included in euro value: stronger control evidence, faster close, reduced rework and improved employee experience.
Improve the process before adding more intelligence
- Improve deterministic matching and exception categorisation
- Introduce workflow and case ownership for exceptions
- Use API integration where practical
- Use RPA only where legacy systems cannot be integrated cleanly
- Test AI assistance for investigation summaries and evidence gathering
- Keep human approval for material financial decisions
Capabilities likely to be required
- Workflow / case orchestration
- API and data integration
- Possible RPA / UI automation
- AI-assisted exception reasoning or summarisation
- Audit trail and human approval controls
Named vendor recommendations should follow only after the technology estate, security, licensing and procurement constraints are understood.
What could weaken the case
- Exception volumes are not measured consistently
- Underlying source-data quality may be the real cause
- Legacy-system access may constrain integration
- Control ownership and materiality thresholds are not yet explicit
- AI should not be used to make autonomous financial judgements
What Oakmere currently knows
How the value changes with the addressable-effort assumption
The scenario range is not a forecast. It shows how the value case moves when the most important assumption changes.
The case is most sensitive to measured investigation effort
If the 3,200 annual investigation hours are overstated, the business case falls quickly. Before investment, Oakmere would prioritise a 4–6 week evidence baseline by exception category rather than treating the current estimate as fact.
What to prove before investment
- Baseline 4-6 weeks of exception volume and effort by category
- Separate avoidable process/data issues from genuine investigation work
- Confirm system access and integration constraints
- Test a representative sample of high-volume exception types
- Agree control boundaries and human approval points
- Recalculate value with measured evidence before vendor selection