Resource 3

AI use cases for finance processes

These scenarios provide a starting point for pilots. Each follows the same structure: objective, AI approach, data, implementation example, impact and control requirements.

26 scenarios 10 domains Designed for piloting

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How to use the catalogue

First, find the domain and check whether the use case falls within a priority area.
Then assess data readiness and human review requirements.
Finally, add the scenario to a backlog with an owner, a baseline metric and a pilot approach.

Use case catalogue

Scenarios are grouped by domain for easier navigation. Expand individual scenarios within each section.

Management Reporting and Business Analysis

Scenarios for commentary generation, anomaly detection and self-service analytics.

Use case 1. AI-generated management commentary

Draft narrative commentary on profit and loss, balance sheet and cash flow after period-end close.

Scenario details

Objective: accelerate preparation of executive commentary.
Inputs: standardised financial reports, historical data and materiality rules.
Approach: an LLM drafts commentary on variances and trends.

Implementation example and impact

BI exports budget-versus-actual tables after close.
FP&A receives draft commentary and refines it.
Impact: faster management pack delivery and less routine analytical work.

Related resources

Function map Priority

Use case 2. AI anomaly detection in management reports

Automatically identify unusual reporting movements and flag possible causes.

Scenario details

Objective: reduce time spent finding variances manually.
Inputs: actuals time series, segments and thresholds.
Approach: rules and models identify outliers and structural shifts.

Implementation example and impact

Weekly reporting automatically generates a list of anomalies.
The controller receives brief machine-generated hypotheses.
Impact: earlier detection of issues and fewer missed anomalies.

Use case 3. Natural-language analytics assistant

Find analytical answers in natural language using an approved semantic layer.

Scenario details

Objective: provide self-service access to figures and explanations.
Inputs: KPI catalogue, definitions, governed data mart and access model.
Approach: an assistant interprets the question and constructs a safe query.

Implementation example and impact

A manager asks about margins and receives a breakdown.
FP&A spends less time on routine ad hoc requests.
Impact: faster analysis and better access to facts across the business.

Treasury and Liquidity Management

Scenarios for cash forecasting, payment prioritisation and covenant early warnings.

Use case 4. Short-term cash forecasting

Forecast cash flow over 1–13 weeks and explain the drivers of changes.

Scenario details

Inputs: bank statements, payable / receivable schedules, payroll and debt calendars.
Approach: machine learning or statistical models with business adjustments.
Controls: backtesting, confidence bands and manual override logs.

Implementation example and impact

The forecast is recalculated daily for each legal entity and the group.
Treasury sees the forecast and the drivers of movements.
Impact: better liquidity planning and earlier detection of cash shortfalls.

Use case 5. Payment prioritization copilot

A recommended payment queue when liquidity is constrained.

Scenario details

Inputs: payment register, liquidity limits, priority rules and penalties.
Approach: scoring by due date, criticality, consequences and policy rules.
Constraint: AI recommends payments but does not execute them.

Implementation example and impact

Each morning, treasury receives a ranked list with explanations.
Impact: faster daily decisions and better cash allocation.

Use case 6. Covenant and liquidity early warning

Early warnings of covenant breach risk and shrinking liquidity headroom.

Scenario details

Inputs: loan agreements, covenant formulas and management forecasts.
Approach: threshold monitoring and scenario projections.
Control: the finance team validates formulas and headroom calculations.

Implementation example and impact

The system flags the risk of reduced EBITDA covenant headroom in advance.
Impact: more time for corrective action and communication with banks.

Procure-to-Pay

Scenarios for invoice extraction, duplicate controls and spend analytics.

Use case 7. Invoice data extraction and validation

OCR and invoice field extraction followed by completeness and consistency checks.

Scenario details

Inputs: PDFs / scans, supplier master data, purchase orders and contracts.
Approach: OCR, a rules engine and an exception queue.
Control: human review for large amounts and new suppliers.

Implementation example and impact

Invoices are recognised automatically and routed to exceptions only when discrepancies arise.
Impact: less manual entry and a faster accounts payable cycle.

Use case 8. Duplicate invoice and payment detection

Find exact and near-duplicate invoices before a payment run.

Inputs: invoice history, payment history and vendor master data.
Approach: similarity matching on amount, date, number and payment details.
Controls: a review queue and a log of the controller's decision.

Example: the system flags suspected duplicates before payment.
Impact: fewer losses and stronger accounts payable controls.

Use case 9. Spend classification and savings analytics

Classify spend and identify fragmented procurement.

Inputs: accounts payable exports, descriptions, supplier master data and cost centre mappings.
Approach: AI classification and clustering by category and owner.
Control: a consistent spend taxonomy.

Example: a controller discovers that one spending category is spread across 17 suppliers.
Impact: support for savings initiatives and supplier consolidation.

Order-to-Cash and Revenue Control

Scenarios for collections scoring, billing quality and dispute handling.

Use case 10. Collection prioritization

Rank customers by likelihood of late payment and the value of the next action.

Inputs: ageing reports, payment history, dispute history and segmentation.
Approach: scoring models and recommended actions.
Controls: human decisions and bias monitoring.

Example: a daily ranked list for the collections team.
Impact: lower overdue receivables and better team workload allocation.

Use case 11. Billing quality checker

Check invoices against orders, contracts, tariffs and service confirmations.

Inputs: order data, pricing rules, contract terms and service confirmations.
Approach: consistency checks and quality flags.
Control: formal rules for commercial exceptions.

Example: AI flags an unauthorised discount or missing service confirmation.
Impact: fewer disputes and less rework.

Use case 12. Dispute triage assistant

Classify reasons for partial payment and non-payment into standard categories.

Inputs: email threads, CRM notes, invoice data and payment data.
Approach: text classification and routing by reason.
Control: access restrictions on customer data.

Example: disputes are automatically categorised as billing errors, missing documents or volume disagreements.
Impact: faster resolution and better root-cause analysis.

Strategy, FP&A, Budgeting and Performance Management

Scenarios for forecasting, variance explanations and scenario planning.

Use case 13. Driver-based forecast assistant

Automatically propose a baseline forecast using a driver tree.

Inputs: historical data, driver tree, sales pipeline and cost assumptions.
Approach: driver-based recalculation and suggested baseline forecasts.
Control: finance overrides for one-off events.

Example: FP&A receives a suggested forecast before the forecasting cycle.
Impact: faster replanning and greater consistency across versions.

Use case 14. Variance explanation assistant

Draft hypotheses explaining variances by line item, business area and driver.

Inputs: budget, actuals, forecast, driver dictionary and business events.
Approach: correlation analysis and narrative hypothesis generation.
Control: explanations remain labelled as hypotheses until validated.

Example: a review pack includes a set of hypotheses for major variances.
Impact: faster performance reviews and less routine analysis.

Use case 15. Scenario simulation copilot

Support downside, upside and stress scenarios with their impact on profit and loss, cash flow and covenants.

Inputs: a validated planning model, drivers, debt and liquidity constraints.
Approach: guided scenario building and impact visualisation.
Control: a single validated planning model is required.

Example: the CFO specifies shocks to revenue and DSO, and the system calculates a downside scenario.
Impact: better decision support under uncertainty.

Record-to-Report and Period Close

Scenarios for close cockpits, journal monitoring and reconciliation assistance.

Use case 16. Close cockpit with AI issue detection

Highlight close blockers and risks to deadlines.

Inputs: close calendar, task statuses, reconciliation logs and journal entries.
Approach: pattern detection for stalled tasks and recurring delays.
Control: named team members retain ownership.

Example: a controller sees that intercompany reconciliation may miss the agreed post-period-end deadline.
Impact: greater control over the close process.

Use case 17. Journal entry anomaly detection

Identify unusual journal entries and manual overrides.

Inputs: ledger data, journal metadata, user activity and adjustment history.
Approach: anomaly detection by amount, time, account and user behaviour.
Control: a review queue rather than automatic blocking.

Example: overnight manual entries to unusual accounts are sent for review.
Impact: stronger record-to-report controls and lower misstatement risk.

Use case 18. Reconciliation assistant

Automatically match transaction-level data across systems.

Inputs: transaction-level data, mapping rules and tolerance settings.
Approach: probabilistic matching and explanations of unmatched items.
Control: regular review of tolerances and rules.

Example: 92% of rows match automatically; the remainder goes for review.
Impact: a shorter close cycle and less manual reconciliation.

Risk Management and Internal Controls

Scenarios for continuous monitoring and fraud pattern detection.

Use case 19. Continuous controls monitoring

Continuously monitor control breaches across finance processes.

Inputs: process logs, approvals, user roles, transactions and the control matrix.
Approach: exception monitoring based on rules and anomaly detection.
Control: a formal investigation and remediation process.

Example: a daily dashboard of approval matrix breaches and vendor changes.
Impact: earlier detection and scalable control coverage.

Use case 20. Fraud risk pattern detection

Find complex links between vendors, bank details, users and transaction timing.

Inputs: vendor master data, bank details, user logs, invoice and payment history.
Approach: graph and pattern analysis of suspicious combinations.
Control: careful investigation procedures and protection against false accusations.

Example: a supplier with duplicate bank details is created and paid on the same day.
Impact: stronger anti-fraud capabilities.

Finance Data, Systems and Governance

Scenarios for data quality monitoring and finance knowledge support.

Use case 21. Data quality monitoring assistant

Rank data incidents by their importance to finance.

Inputs: ETL logs, mapping tables, control totals and data lineage.
Approach: detection of missing loads, inconsistencies and structural changes.
Control: basic technical checks before adding the AI layer.

Example: the system flags an incomplete CRM load before morning reporting.
Impact: more reliable data and less manual root-cause investigation.

Use case 22. Finance knowledge assistant

A retrieval-augmented assistant for policies, KPI definitions, procedures and templates.

Inputs: policy documents, metric catalogue, process manuals and approved templates.
Approach: source-grounded questions and answers with references.
Control: approved sources only, with document citations.

Example: a controller checks the definition of adjusted EBITDA and the rules for intercompany reconciliations.
Impact: faster onboarding and fewer misinterpretations of rules.

Corporate Finance, Banks and Stakeholder Communications

Scenarios for bank reporting packs and investment memos.

Use case 23. Bank pack and covenant narrative generator

Draft bank reporting packs and explanations of covenant metrics.

Inputs: covenant data, management reporting, financing agreements and pack templates.
Approach: a first-draft pack with explanatory notes.
Controls: approved figures only and a mandatory reviewer.

Example: a treasury manager receives a prepared draft of the monthly bank reporting pack.
Impact: faster delivery and more consistent external communications.

Use case 24. Investment memo drafting assistant

Automatically assemble a structured capital expenditure / investment memo.

Inputs: business case assumptions, ROI / IRR outputs, scenarios and risk notes.
Approach: structure the narrative of a decision memo.
Control: the finance owner validates all conclusions and calculations.

Example: the initiator uploads assumptions and the system drafts the memo.
Impact: better comparability of investment cases.

Tax, Compliance and Statutory Contour

Scenarios for evidence retrieval and completeness checks under strict control.

Use case 25. Audit and compliance evidence retrieval

Find and assemble supporting documents, journal entries and approval trails into one evidence pack.

Inputs: document archive, metadata, accounting records and links between documents and transactions.
Approach: retrieve and package related evidence.
Controls: strict access rules and a completeness review.

Example: an audit request is answered with an automatically assembled evidence pack.
Impact: less manual searching and faster responses.

Use case 26. Tax document completeness checker

Check tax supporting documents for completeness and inconsistencies before filing.

Inputs: tax forms, supporting documents, policy rules and accounting references.
Approach: completeness and consistency checks.
Control: AI does not replace tax judgement.

Example: the system flags missing evidence before the filing deadline.
Impact: less last-minute rework and fewer coordination errors.

Common candidates for a first pilot

For a quick start without redesigning the entire architecture, these use cases are often the most practical.

Reporting

Use case 1 and 2 deliver quick results without requiring autonomous decisions.

Treasury

Use case 4 and 5 create visible cash impact when reliable data flows are available.

Controls

Use case 7, 10 and 17 are well suited to supervised pilots.