Solution / Business use case
Prepared evidence. Faster close. Controllers decide.
Reconciliation support, policy checks and evidence gathering, with controllers firmly in charge.
Finance & Compliance
/ 01Challenge
What gets in the way today
Finance and compliance teams spend much of their time collecting evidence, matching records and checking transactions against policies. Month-end close and audits compress that work into stressful peaks.
These are controlled processes. Any AI involvement must be explainable, auditable and subordinate to qualified people.
/ 02AI workflow
How the work flows with AI in it
Steps marked human are decision points owned by your people.
- 01
Collect
Transactions, statements, policies and supporting documents are gathered.
- 02
Match and check
Records are matched and checked against policies and thresholds.
- 03
Explain
Exceptions get a written explanation with linked evidence.
- 04human
Decide
Controllers or compliance officers accept, reject or investigate each exception.
- 05
Report
Decisions and evidence are compiled into audit-ready records.
/ 03Example architecture
Signals in, governed outcomes out
A typical reference design. Your version depends on your systems, data and risk appetite.
- ERP and ledger data
- Bank and card statements
- Policies and controls
- Supporting documents
- Rule-based matching
- Anomaly detection
- Explanation drafting
- Evidence linking
- Segregation of duties
- Human decision on every exception
- Immutable audit log
- Read-only data access
- Exception queue with evidence
- Audit trail
- Close and compliance reports
/ 04Benefits
What changes, and how you would measure it
Shorter close cycles
Matching and evidence collection happen continuously instead of at month-end.
Stronger audit trail
Every exception carries evidence and a recorded decision.
Focused expertise
Specialists spend time on judgement rather than collation.
Measures we would agree with you
- Days to close
- Exception review time
- Share of transactions auto-matched
- Audit request turnaround
We do not quote generic improvement figures. Targets are set from your measured baseline.
Limitations to plan for
- AI outputs support decisions; they do not replace accountable sign-off.
- Regulatory interpretation requires qualified professionals and, where relevant, legal advice.
/ 05Product and service match
What we would use
Product concepts
- Product conceptDocument IntelligenceClassification and field extraction for contracts, invoices and forms, with validation and human review.
- Product conceptAI Analytics CopilotAsk questions of governed business data in plain language and see the query behind every answer.
- Product conceptWorkflow Automation HubAI steps inside dependable workflows: triggers, rules, models and approvals in one orchestrated flow.
Services
- Service / adviseResponsible AI & GovernancePractical AI governance: policies, risk assessment and controls that teams can follow without stopping delivery.
- Service / integrateData Engineering & AI PipelinesThe data foundations AI needs: reliable pipelines, clean models, governed access and lineage.
- Service / operateSecurity, Privacy & DeploymentDeploy AI where your data rules require, with threat modelling, privacy controls and hardened infrastructure.
/ 07FAQ
Questions buyers ask
Can AI approve transactions?
We do not design systems where AI approves financial transactions on its own. It prepares evidence and recommendations; accountable people decide.
How do auditors view AI-assisted processes?
Auditors generally expect documented controls, evidence of testing and clear accountability. Our designs produce that documentation as part of the system.
Explore AI for finance & compliance
Bring a sample of the work: tickets, documents, requests. We will show you what is realistic and how we would measure it.