GenAI in Finance Is Now a Controls Issue, Not a Tech Trend

GenAI has entered finance the way spreadsheets once did: quietly, usefully, and faster than policy can keep up. I see it in moments that are not small at all—drafting close commentary, summarisingcontracts, and preparing first-pass responses to auditors. The output is fluent, confident, and once pasted into a deck, it becomes part of the record.

This is why GenAI is no longer an IT experiment. For finance leaders, it is a governance and internal controls issue. The question is not whether GenAI will be used—it already is. The question is whether it will be used in a way that preserves three things finance cannot compromise: evidence, confidentiality, and accountability.

Why GenAI Is Now a Finance Governance Problem

Finance runs on disciplined processes: close, reporting, controls testing, audit support, and board communication. GenAI touches all of them and creates three predictable risks when governance is missing.

1. Evidence dilution

AIassisted narratives still require a defensible trail — inputs, assumptions, review, and retention. Without this, evidence becomes fragile.

2. Data boundary leakage

Finance teams handle forecasts, payroll, vendor banking, tax positions, and deal materials. None of this should enter unapproved tools or personal accounts.

3. Accountability blur

GenAI can sound right even when it is wrong. If teams accept output because it “reads well,” the control environment weakens until an auditor or regulator challenges it.

GenAI can raise productivity — but unmanaged GenAI can also manufacture a new class of control failures:wellwritten ones.

The Finance GenAI Playbook: ControlsbyDesign

I view GenAI adoption as a change to the finance operating model. The right response is to govern it—so speed does not come at the cost of trust.

1) A onepage “permitted / conditional / prohibited” policy

Encourage lowrisk uses (drafting general narratives). Allow highimpact uses only with controls. Prohibit confidential financials, payroll, bank details, deal materials, or tax positions in nonapproved tools.

2) Make human ownership explicit

GenAI assists; finance signs. Every AIassisted output that influences reporting, controls, audit responses, or external communication must have a named owner who ties numbers to source reports and approves the final text.

3) Restrict the toolset and control access

Approve a small set of enterprise platforms and require their use. Apply rolebased access and segregation for sensitive deliverables. “Personal accounts because it’s faster” is not a control environment.

4) Add an “AI review” checkpoint where risk is highest

Focus on close narratives, audit responses, significant accounting conclusions, and any content that could influence disclosure. A simple checklist — source tieout, assumption check, confidentiality check, reviewer signoff — prevents fluent errors from becoming official records.

5) Treat prompts and outputs as control artifacts

For highimpact use cases, require prompts to reference approved sources and require outputs to state assumptions. If the output cannot explain its inputs, it cannot be relied on.

6) Bring Internal Audit in early

Internal Audit should validate whether governance works in practice: approved tool usage, evidence tieouts, adherence to prohibited data boundaries, and whether reviewers challenge outputs rather than rubberstamping.

The India–US Lens: Distributed Finance Raises the Stakes

India–US finance models often combine U.S. leadership with Indiabased shared services or captive teams executing close, reporting, analytics, and controls work. GenAI can multiply productivity across this model by standardizing drafting and accelerating analysis — but only if governance is consistent across locations.

The risks rise when India teams generate firstdraft narratives, U.S. teams assume the draft is fully validated, or prompts contain sensitive data. Fluency cannot replace evidence. Uniform standards, strict data boundaries, and clear draft–review–approve roles are essential for distributed teams to use GenAI safely and credibly.

Board Takeaway

Boards do not need to understand prompts — they need to trust the process. A onepage update should make four things unambiguous: where GenAI is used (and where it is not), what guardrails protect confidential data, what review controls ensure accuracy, and what Internal Audit has validated with remediation underway. If you can answer those clearly, you are governing GenAI as a finance capability — not treating it as a novelty.

The Leadership Imperative

GenAI will keep evolving. The finance organizations that win will not be the ones chasing every new tool, but the ones building trust the fastest by pairing speed with governance, and innovation with evidence. That is how finance leaders turn GenAI from a productivity experiment into a durable, auditready capability.

 The article is authored by By Srajan Singhai, Global Internal Auditor and Finance Leader