The opinions expressed here are those of the authors. They do not necessarily reflect the views or positions of UK Finance or its members.

From automating reconciliations and journal entries to supporting regulatory reporting, forecasting and control testing, AI offers the potential to reduce manual effort, improve accuracy and accelerate close cycles. For CFOs, Treasurers and finance leaders under constant pressure to do more with less, the opportunity is compelling. But so are the risks.

Unlike front-office innovation, back-office finance sits at the heart of regulatory accountability. Financial statements, capital calculations and regulatory submissions must be accurate, auditable and defensible. Introducing AI into these processes raises an immediate question: how can organisations harness AI's efficiency gains without exposing themselves to regulatory, operational and reputational harm?

One critical risk is hallucination. Generative AI tools used to draft technical accounting memos, interpret regulatory requirements or summarise policy updates can produce outputs that appear authoritative but contain subtle inaccuracies. In a financial and internal management reporting context, even minor errors can cascade into material misstatements. Finance teams must treat AI-generated content as a starting point, not a final product, with robust review and validation embedded into workflows.

Model reliance presents another challenge. If staff begin to default to AI-generated reconciliations, variance analyses or forecasts without understanding underlying assumptions, control effectiveness weakens. Regulators increasingly expect transparency around automated decision-making. Finance functions must maintain clear documentation of model logic, data lineage and human intervention points to demonstrate control integrity.

Prompt governance is an emerging but essential discipline in this environment. The way users frame instructions to AI systems directly influences outputs. Uncontrolled prompts can inadvertently expose confidential financial data, generate non-compliant disclosures or introduce bias into analytical outputs. Formal governance over approved use cases, prompt libraries, testing protocols and monitoring mechanisms is becoming as important as traditional IT controls.

Liability considerations also loom large. If an AI-supported regulatory filing proves inaccurate, accountability ultimately sits with the firm. Clear ownership structures, defined approval checkpoints and strong vendor management frameworks are necessary to manage third-party model risk.

The defining factor in balancing innovation and risk is the operating model. Responsible AI deployment in the finance back office requires integrated governance across finance, treasury, risk, compliance, IT and internal audit. It demands clear policies on acceptable use, rigorous model validation, continuous performance monitoring and targeted training to build AI literacy within finance teams.

In highly regulated environments, AI cannot operate as an experimental side project. It must be embedded within a controlled, auditable framework. The firms that succeed will not simply automate faster - they will design operating models that protect trust, reinforce accountability and enable innovation without compromising control. Join us on the Wednesday 18^th March 2026, at Board Beyond 2026, where we will be discussing these themes.