From Finance Questions to Reviewable Decisions: An Evidence-First AI Workflow
See how an evidence-first workflow can help finance teams investigate spend, review recommendations, and keep accountable decisions in human hands.
Most finance problems do not arrive as neat questions with one obvious answer.
They arrive as a deadline.
It is four days before month-end. A growth team depends on a Google Ads card to keep campaigns running. Spend is pacing above last month, and the card may reach its limit before the reporting period closes.
The finance team has several imperfect options:
- leave the limit unchanged and risk interrupting an active campaign;
- raise it without enough context and weaken spending controls; or
- pause, investigate the underlying records, check the policy, and decide what is justified.
This is where many AI tools stop too early. They can summarize the situation, but a summary alone is not a finance workflow. The person approving the change still needs to know which records were checked, which policy applies, what the proposed action changes, and whether it can be reversed.
The useful question is not simply, “What should we do?”
It is: “What should we do, based on which evidence, under whose authority, and with what controls?”
Why a plausible answer is not enough
Imagine an assistant responding:
Raise the card limit so the campaign can continue.
That may sound reasonable, but it leaves the reviewer with more work:
- How quickly is spending increasing?
- Is this a normal seasonal change or an exception?
- What is the current card limit?
- Does company policy permit an increase?
- How long should the new limit remain active?
- Who is authorised to approve it?
- What record will remain after the decision?
Without these details, AI has generated a suggestion—not a reviewable decision.
An evidence-first workflow closes that gap by keeping the answer connected to the records, permissions, policies and approval steps behind it.
A better workflow: from signal to controlled action
A useful finance workflow should do four things.
1. Detect the issue early
The best time to find a spending problem is before it becomes an operational problem.
Instead of waiting for a declined payment or discovering an overage during close, the system can surface unusual pacing, missing information or a possible duplicate while there is still time to review it.
2. Bring the relevant evidence together
The reviewer should not have to open a card platform, export a ledger, search a policy folder and reconstruct recent spending manually.
The workspace should bring the supporting context together: the relevant card, ledger activity, applicable policy and recent run rate.
3. Explain the recommendation
A recommendation becomes more useful when the reviewer can inspect why it was made.
For example, a proposed temporary limit increase might be based on current pacing, the remaining days in the month and the ceiling allowed by policy. The recommendation should make those inputs visible instead of hiding them behind a confidence score.
4. Keep the final action in human hands
Finance teams remain accountable for consequential decisions. AI can prepare the investigation and recommend a next step, but an authorised person should review and approve the action.
Illustrative Lavas interface. Example company, users and values are fictional.
How Lavas turns the investigation into a workspace
In the example above, Lavas Cortex does not only say that the Google Ads card may reach its limit.
It presents the issue in a reviewable workspace:
- the expected date on which the card may reach its limit;
- the change in spending pace;
- the existing limit and recommended temporary limit;
- the policy ceiling relevant to the decision;
- the sources checked, including the ledger, card and recent run rate;
- the action available to the authorised reviewer; and
- the planned reversion after month-end.
The reviewer can inspect the reasoning, open the approval detail and decide whether to proceed.
That is a much more useful role for AI in finance: reduce the time spent gathering context while preserving the controls around the decision.
The same pattern applies to accounts payable
Consider a second problem: a draft invoice looks similar to an invoice already in the payment queue.
A generic AI assistant might label it a duplicate. But invoices can resemble one another for legitimate reasons—recurring services, partial billing or multiple invoices issued under the same purchase order.
A safer workflow flags the potential risk and asks the reviewer to confirm the relevant details before money moves.
Illustrative Lavas interface. Potential risks are surfaced for review rather than treated as final determinations.
In Lavas Bill Pay, teams can keep intake, approval, payment readiness and history in one workflow. A possible duplicate can be surfaced alongside the vendor, amount, invoice number, status and next action.
The objective is not to remove judgement. It is to make the judgement faster and better informed.
Expense review needs context too
Expense operations create a similar challenge at a different scale.
A finance team may need to review card transactions across travel, advertising, software and infrastructure. Some transactions are ready to approve. Others need a receipt, category confirmation or additional review.
When the context is scattered, the team spends its time chasing documents and switching systems instead of resolving exceptions.
Illustrative Lavas interface showing a consolidated expense-review queue.
Lavas brings transactions and their review state into one workspace, so teams can focus attention on the items that need it. The system can organise incoming information and surface exceptions, while the finance team retains control over approval and accounting treatment.
A practical way to introduce AI into finance operations
Teams do not need to automate an entire finance function at once. A narrower workflow is easier to govern and easier to evaluate.
Start with one recurring decision:
- Choose a high-friction question. Look for work that repeatedly requires people to search several systems or assemble the same evidence.
- Identify the authoritative records. Define which transactions, invoices, cards, policies and approvals are needed.
- Preserve existing permissions. The AI workflow should respect the same access boundaries as the underlying systems.
- Require visible evidence. Reviewers should be able to inspect the sources behind a recommendation.
- Define the human decision point. Make it explicit who can approve, reject or request more information.
- Keep an audit trail. Record the issue, supporting context, recommendation and final action.
- Measure the outcome. Track investigation time, exception resolution and the number of manual system switches—not just the number of AI responses.
What a successful workflow should feel like
The best finance AI experience is not dramatic.
It feels like less searching, fewer unexplained recommendations and a clearer path from issue to resolution.
A reviewer should be able to answer:
- What happened?
- Why was it flagged?
- Which records support the finding?
- Which policy or control applies?
- What action is being proposed?
- Who remains accountable for the decision?
If those questions are easy to answer, AI is no longer operating as a detached chatbot. It has become part of a controlled finance workflow.
Put evidence closer to every finance decision
Lavas brings spend management, expenses, bill pay, policy context and AI-assisted review into a connected workspace. Cortex helps surface issues, organise supporting evidence and prepare next actions for authorised reviewers.
The goal is not to replace the people responsible for finance. It is to give them better context before they decide.