Agentic AI in finance and accounting

Agentic AI is moving finance and accounting from task automation to autonomous workflow execution and insights generation on the fly. Where earlier AI tools required human instruction at each step, AI agents assess situations, make decisions, and act independently across connected systems. Accounting and Finance teams can execute much faster, at scale, but this also raises fundamental control and governance questions.

Summary

• Agentic AI goes beyond traditional automation by enabling AI systems to reason, plan, and take multi-step actions autonomously, without requiring human intervention at each stage.
• 80.5% of finance and accounting professionals say AI agents could become standard tools within five years, per Deloitte’s 2025 poll of 3,300 professionals.
• Only 13.5% of organisations are already using agentic AI in finance, but 33.6% are building or planning to adopt.
• 72% of finance leaders cite operational efficiency as the top benefit, and 90% agree agentic AI will improve key business processes, based on UiPath’s 2025 Agentic AI Report.
• Adoption is accelerating but a clear implementation strategy is essential; broad AI adoption without targeted use cases consistently underdelivers on ROI.
• Risks including data governance, security, and model errors require structured oversight frameworks before autonomous agents are deployed in high-value financial workflows.

What is agentic AI?

Agentic AI refers to AI systems capable of autonomous, multi-step decision-making. Unlike traditional AI tools that respond to a single input and return a single output, AI agents can set sub-goals, use tools, call on external data sources, and take sequences of actions to complete complex tasks, adapting their approach as circumstances change.

In practical terms, an AI agent does not just flag a discrepancy in a bank reconciliation. It investigates the discrepancy, cross-references the relevant ledger entries, checks for matching transactions across connected systems, and proposes or enacts a resolution. The human reviews and approves the outcome rather than performing each step.

How is agentic AI transforming finance and accounting?

The shift is most visible in three areas. 

First, automation is transforming reconciliation from a periodic, manual process to a continuous, system-driven one.

Second, agentic AI is enabling finance teams to act on exceptions rather than process routine transactions, freeing time for analysis and decision support. 

Third, AI agents are beginning to orchestrate across previously siloed tools, connecting ERP platforms, bank feeds, AR systems, and reporting tools into integrated, end-to-end workflows. A broader look at AI tools for finance teams shows how quickly this landscape is moving.

Use cases of agentic AI in finance

Automated reconciliation. AI agents match transactions across bank statements, sub-ledgers, and ERP systems continuously, flagging only the exceptions that require human judgement. Transforming the reconciliation process in this way has a direct impact on close speed, data accuracy, and audit readiness.

Accounts receivable automation. AI agents monitor invoice status, send escalating payment reminders based on customer behaviour, match incoming payments to open invoices, and route unresolved cases for review, all without manual instruction at each step. This reduces DSO and the volume of unapplied cash sitting on the ledger.

Real-time decision support. Rather than waiting for period-end reports, AI agents continuously aggregate and analyse financial data, surfacing cash flow risks, variance anomalies, and budget deviations as they emerge. Finance leaders move from reactive reporting to proactive oversight.

Fraud and compliance detection. AI agents monitor transaction patterns against established behavioural baselines and external factors, flagging out-of-pattern activity in real time. This is particularly relevant under FCA expectations around operational resilience and financial crime controls, where firms are expected to detect and respond to anomalies promptly.

Data-informed budgeting and forecasting. AI agents can pull actuals from multiple systems, compare them against forecasts, identify variances, and update forward-looking models continuously, replacing the manual consolidation that typically delays budget reviews and financial planning by days.

Agentic payments. Payment agents can validate invoice details against purchase orders, apply approval rules, and initiate payment within defined parameters, reducing the manual touchpoints in the AP cycle while maintaining segregation of duties through system-enforced controls.

The risks of agentic AI in finance

Governance and accountability. When an AI agent takes an action autonomously, it must be traceable. The FCA’s expectations around model risk and operational resilience require that firms can explain and audit the decisions made by automated systems, and prove that adequate human approvals were in place. An agent that cannot produce a clear audit trail, including human validation points, introduces compliance risk rather than reducing it.

Data quality and model errors. Agentic AI is only as reliable as the data it acts on. Poor-quality inputs (inconsistent formats, duplicate records, outdated master data) can lead agents to take incorrect actions at scale. Unlike a human who might pause when something looks wrong, an agent will continue processing. Investing in data hygiene before deploying AI agents is not optional: the firms that see the most from agentic AI are consistently those that resolve data quality issues upstream.

Security and access controls. AI agents that operate across multiple systems require carefully scoped permissions. Organisations deploying agentic AI in finance need to apply the same segregation of duties principles to agent permissions that they apply to human roles, ensuring no agent can both initiate and approve a high-value action without a human control checkpoint.

Agentic AI will deliver significant value in finance, but the firms that see the most from it will be the ones that treat governance as a design principle rather than an afterthought. The question is not just what the agent can do — it is what it is permitted to do, and how every action is explained and audited.

Clément Bourcart, Product & Solutions Manager, Aurum Solutions

How Aurum is transforming reconciliation with automation and AI

At Aurum, we work with finance teams managing complex, high-volume reconciliation across multiple entities, currencies, and data sources. Our platform connects to ERP systems, bank feeds, and payment processors, automating the matching process and surfacing exceptions for human review rather than requiring manual intervention across every transaction.

Finance teams using Aurum reduce the time spent on manual reconciliation significantly, close their books faster, and maintain audit-ready records throughout the period. 

As automated reconciliation matures into AI-driven exception management, Aurum’s approach positions finance teams to operate at the next level of autonomous finance without sacrificing the control and transparency that regulators and auditors require.

Book a demo with Aurum to see how data automation and AI can reduce pain points in your finance and accounting operations.

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