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If an AI tool prepares a forecast, flags an unusual transaction, or drafts a management report, who in the organisation is prepared to stand behind the output?
Finance is a Trust Function before it is an Efficiency Function
Artificial intelligence is already entering finance functions through invoice processing, forecasting models, anomaly detection, reporting assistants, and generative AI copilots. In Singapore, Budget 2026 identifies AI as a strategic advantage, while ACRA's refreshed Skills Framework embeds AI competencies across accountancy roles, reflecting the profession's shift towards higher-value, data-driven work.
Yet finance is not just another back-office function. It supports financial reporting, management decisions, tax, regulatory compliance, and stakeholder confidence. This is why companies should not treat AI in finance purely as an efficiency initiative. The more important question is whether management can demonstrate, with evidence, that AI-generated outputs are reliable, explainable, and appropriately supervised. Companies should therefore integrate AI into finance in a controlled, finance-led, and assurance-ready manner.
Start with Finance Problems, then Classify Risk
Finance leaders should start with the business problem, not the tool. Practical starting points include invoice capture, bank reconciliation, expense claim review, duplicate payment detection, anomaly detection, variance analysis, and management reporting drafts. These use cases are relatively contained, involving repetitive tasks, structured data, and clear review points.
Companies should be more cautious when AI influences revenue recognition, impairment assessment, going concern analysis, tax positions, or financial statement disclosures. In these areas, AI can support analysis, but accountability must remain clearly with management.
Not all finance AI use cases carry the same risk. Companies should classify them based on financial impact, level of judgement involved, data sensitivity, degree of automation, and impact on reporting or controls. The aim is not to slow innovation, but to avoid scaling AI faster than the organisation can govern it.
The Assurance Opportunity: Readiness before Reliance
AI assurance should not be positioned narrowly as a technical model validation exercise. The more practical need, especially in the Singapore mid-market, is around readiness and control maturity. Companies need to know what AI tools are being used, what data feeds them, who reviews outputs, how exceptions are handled, and whether there is sufficient documentation to support reliance.
This aligns with Singapore's AI governance direction. MAS' proposed AI risk management guidelines emphasise oversight, lifecycle controls, and capability building. IMDA's Model AI Governance Framework for Agentic AI similarly highlights responsible deployment and human accountability.
Finance leaders should be able to answer key questions: What data does the AI use? Who reviews the output? What happens when the AI is wrong? Can the decision trail be reproduced? These are familiar questions for assurance professionals. They are also the questions management must answer before AI outputs can be relied upon.
Data and Third-Party Risk cannot be Afterthoughts
AI in finance is only as reliable as the data beneath it. Many finance functions still rely on fragmented systems, manual spreadsheet adjustments, inconsistent master data, and undocumented transformations. If AI is layered on top of weak data processes, it may make unreliable data appear more sophisticated. Data lineage should therefore be treated as a core control, particularly when AI supports management reporting, forecasting, or regulatory submissions.
Third-party risk is equally important. Most companies will adopt AI through ERP systems, accounting tools, procurement platforms, and generative AI applications rather than building models from scratch. Finance leaders should understand what vendor AI tools do, what data they process, where data is stored, and what audit logs are available. For many mid-market companies, AI risk may sit heavily within vendor-managed tools, making third-party assurance as important as assurance over internal systems.
AI Assurance as a Natural Extension of Assurance
A realistic first step for most organisations is an AI assurance readiness review — covering the company's AI inventory, finance AI use cases by risk, governance gaps, data flows, third-party AI tools, and sufficient evidence to back AI outputs. Process understanding, internal controls, audit evidence, risk assessment, and professional scepticism are already central to assurance work, making AI assurance a natural extension into a new area of business reliance.
AI in Finance will Scale only as Fast as Trust allows
AI can reduce manual work and help finance professionals focus on higher-value analysis and advisory. However, finance leaders should not confuse adoption with reliance. A company may use AI quickly, but it can only rely on AI when there is sufficient governance, control, and evidence around it.
The companies that benefit most will not necessarily be those that adopt the most tools first. They will be those that integrate AI into finance in a way that is controlled, explainable, and assurance-ready. For assurance providers, this creates a timely opportunity to bridge technology, controls, data, and business risk, helping organisations build confidence in the systems and information that stakeholders rely on.
