Applied AI

Applied AI in Finance

These are selected examples from live finance operations. Each use case separates the repeatable work handled by rules, the defined task given to AI and the decision that remains with finance.

Framework

Finance problem first. Technology follows.

Start with the finance decision

Define the problem, the decision and who owns it.

Build a reliable foundation

Use governed data and clear, repeatable rules.

Give AI a defined task

Use AI to investigate, classify or draft within set boundaries.

Keep finance accountable

A finance professional reviews the output and owns the final decision.

Deployed finance solution

Variance analysis

Controllers spend significant time calculating movements, locating supporting drivers and rewriting commentary across reporting cycles.

How it works

Calculates actual-versus-budget or forecast movements, applies materiality, links driver evidence and prepares a first explanation for review.

Traditional approach: Analyst extracts data, rebuilds bridge calculations and writes commentary manually.
Where deterministic rules are used: Actual-versus-plan calculations, period checks, materiality thresholds and driver links.
Where AI is used: Summarise evidence and draft a reviewable explanation of the movement.
Human control point: The controller validates the drivers and approves the commentary before reporting.
Expected or observed outcome: Expected outcome: less manual commentary preparation and more time for controller challenge.

Deterministic: Variance calculation and driver attribution
AI-assisted: First-draft commentary
Human: Controller review before anything is reported

Operating value

Approximately 60% less manual effort to prepare commentary, creating more time for controller challenge and business discussion while keeping the numbers and evidence visible.

Deployed finance solution

Intercompany reconciliation

Reciprocal balances arrive in different formats and currencies, while unresolved breaks move between entities without a consistent evidence trail.

How it works

Maps entity ledgers, applies reciprocal matching and tolerance rules, isolates breaks and routes each exception with its supporting records.

Traditional approach: Teams compare extracts manually and resolve breaks through fragmented follow-up.
Where deterministic rules are used: Entity mapping, currency normalisation, reciprocal matching and tolerances.
Where AI is used: Group recurring breaks and suggest likely classifications for review.
Human control point: The entity owner resolves the break and records the sign-off evidence.
Expected or observed outcome: Expected outcome: a shorter review focused on genuine breaks with a consistent evidence trail.

Deterministic: Matching rules and break identification
AI-assisted: Break classification and routing
Human: Entity owner resolves and signs off

Operating value

A shorter, more controlled review focused on genuine breaks rather than manually comparing every line.

Deployed finance solution

Finance policy interpretation

Distributed finance teams can interpret the same accounting or process policy differently when answers depend on memory or informal guidance.

How it works

Retrieves the relevant approved policy text, attaches the governing clause and drafts a plain-language response with an escalation path for judgmental cases.

Traditional approach: Finance teams search policy documents or ask specialists for repeated interpretations.
Where deterministic rules are used: Retrieval from approved policy content and citation of the governing clause.
Where AI is used: Translate the approved policy into a plain-language draft and identify ambiguity.
Human control point: Technical accounting reviews judgmental cases and owns the conclusion.
Expected or observed outcome: Expected outcome: faster access to consistent policy guidance without hiding the source.

Deterministic: Retrieval of the governing policy text
AI-assisted: Plain-language interpretation
Human: Technical accounting judgment on close calls

Operating value

Faster policy access and more consistent treatment without hiding the authoritative source.

Deployed finance solution

Order-to-cash workflow

Order, billing and collection activities can stall at hand-offs, while teams spend time chasing normal transactions and genuine exceptions together.

How it works

Tracks the defined order-to-cash stages, completes repeatable status actions and prioritises disputed, aged or incomplete items for review.

Traditional approach: Teams chase normal and exceptional transactions together across hand-offs.
Where deterministic rules are used: Stage completion, ageing thresholds, dispute flags and missing-field checks.
Where AI is used: Prioritise exceptions and draft the next-action context for the owner.
Human control point: Finance decides how disputed or aged items should be resolved.
Expected or observed outcome: Expected outcome: clearer ownership of items affecting billing, cash or customer resolution.

Deterministic: Workflow execution and status control
AI-assisted: Exception prioritisation
Human: Decision on disputed and aged items

Operating value

Less manual coordination and clearer ownership of the items that can affect billing, cash or customer resolution.

Related thinking

The operating principles behind the work.

View all insights

AI+ Finance6 min read

Rules, LLM or Agent? Choosing the Right Automation for Finance

A practical framework for choosing between deterministic automation, generative AI and controlled agentic workflows in finance.

Finance Operations5 min read

Finance AI Should Start With Exception Management

Modern finance operations improve when teams organise work around material exceptions instead of treating every transaction equally.

The operating rule

Auditability is part of the design.

The workflow should show the rule applied, the evidence used and the person who approved the output. Finance must be able to explain how the conclusion was reached.