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AI
23 Jan 2026
11 min read
Finance teams have spent the last two decades adapting to waves of new technology. From the introduction of ERP systems and cloud accounting platforms to real-time reporting and advanced analytics, each step has promised greater efficiency, accuracy and control.
Yet for many organisations, the reality is that finance functions are still heavily burdened by manual processes, fragmented data and time-consuming coordination across systems. Even with automation in place, teams often find themselves overseeing workflows rather than focusing on strategic insight.
This is where agentic AI for finance marks a meaningful shift. Rather than simply automating individual tasks, agentic AI introduces systems that can plan, act and adapt with a clear objective in mind. These AI agents take responsibility for defined outcomes across end-to-end finance processes, operating autonomously within clear guardrails.
For finance leaders under pressure to deliver faster insight, stronger governance and greater value to the business, agentic AI represents the next stage in the evolution of financial operations.
To understand why agentic AI matters, it helps to look at how finance technology has evolved.
Early digital tools focused on record-keeping. Later, automation reduced repetitive manual effort, such as data entry, reconciliations and invoice processing. More recently, analytics and AI-driven forecasting have improved visibility and decision support.
But even with these advances, finance teams are still required to:
Traditional automation and AI tools remain largely reactive. They execute predefined rules or respond to prompts, but they don’t own the wider process. Agentic AI changes that dynamic.
Agentic AI refers to artificial intelligence systems designed to operate as autonomous agents. In a finance context, these agents are given defined goals and the authority to act across systems to achieve them.
Instead of waiting for instructions at each step, an agentic AI system can:
This represents a shift from automation to autonomy. Where automation focuses on tasks, agentic AI focuses on outcomes. At its core, agentic AI introduces outcome-oriented autonomy. Rather than executing isolated tasks, AI agents are accountable for achieving defined financial outcomes, such as completing reconciliations, maintaining compliance or responding to risk events.
In finance, this autonomy must always operate within strict guardrails. Agentic AI systems are designed with clearly defined permissions, segregation of duties, full auditability and human-in-the-loop validation at defined checkpoints, ensuring control, compliance and accountability are maintained.
The growing interest in agentic AI for finance is driven by a combination of technological maturity and mounting business pressure. Advances in large language models, orchestration frameworks and secure system integrations now make it possible for AI to reason, plan and act across complex financial environments. At the same time, finance leaders are being asked to deliver faster insight, stronger governance and greater strategic value, often without increasing headcount.
AI adoption within finance functions is already well underway. According to KPMG’s Global AI in Finance Report, 71% of organisations are using AI within their finance operations, with 41% deploying it to a moderate or significant degree. This level of adoption shows that AI has moved beyond the experimentation phase and is becoming embedded in core finance activities, creating the conditions for more autonomous, outcome-driven systems.
At an enterprise level, many organisations are now experimenting with agentic AI systems that can plan and execute multi-step workflows. McKinsey’s latest State of AI research shows that most organisations are now experimenting with advanced AI systems that go further than simple rule-based automation. While large-scale deployment remains limited to early movers, this experimentation reflects growing confidence in AI’s ability to support more autonomous, outcome-driven processes/
Together, these trends explain why agentic AI is emerging now. As AI becomes a trusted part of finance operations and organisations grow more comfortable with autonomous decision-making, the move from isolated automation to agentic, end-to-end financial workflows becomes both possible and increasingly necessary.
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Agentic AI is already being applied across a range of finance activities, advancing the applications beyond isolated automation to coordinated workflows.
Transaction processing and reconciliation
Reconciliation is one of the most time-consuming activities in finance. Traditional automation can match transactions based on rules, but exceptions still require manual investigation.
An agentic AI system can take responsibility for the full reconciliation cycle. It can identify mismatches, investigate underlying causes, request additional information where needed, escalate unresolved issues and ensure documentation is complete for audit purposes. The outcome is not only faster reconciliation, but fewer errors and clearer accountability.
Forecasting and financial planning
Forecasting is an area where AI has been used for years, but often as a decision-support tool rather than an active participant.
Agentic AI can continuously monitor changes in revenue, costs and external factors, update forecasts in real time and trigger actions when thresholds are breached. That might include notifying stakeholders, adjusting budgets with role-based approvals, or initiating scenario analysis automatically. This allows finance teams to move from static forecasting cycles to ongoing financial intelligence.
Compliance and regulatory monitoring
Regulatory compliance is becoming more complex, particularly in highly regulated sectors. Agentic AI can monitor transactions, policies and controls continuously, identifying potential compliance risks as they emerge rather than after the fact.
An agent can flag issues, gather supporting evidence, document decisions and prepare audit trails, all while escalating only the cases that genuinely require human judgement. This proactive approach reduces risk while freeing up finance professionals to focus on higher-value oversight.
Fraud detection and risk management
Fraud detection has traditionally relied on rules-based systems and retrospective analysis. Agentic AI enhances this by combining pattern recognition with autonomous investigation.
An agent can detect unusual activity, assess context, cross-reference data sources and decide whether to escalate, block or monitor transactions further. Over time, it learns from outcomes, improving accuracy without constant reconfiguration.
The case for more adaptive, intelligent approaches to risk management is increasingly clear. According to PwC’s Global Economic Crime and Fraud Survey, 42% of organisations either lack a third-party risk management programme altogether or do not carry out risk scoring as part of their existing approach. This highlights a significant gap between the complexity of modern financial ecosystems and the tools many organisations currently rely on to manage risk.
Agentic AI offers a way to close that gap by continuously monitoring third-party data, assessing risk in real time and escalating issues as they emerge, rather than relying on periodic reviews or manual checks.
Finance-led customer service
In some organisations, finance teams are increasingly involved in customer-facing processes, such as billing queries or credit management.
Agentic AI can manage the end-to-end lifecycle of these interactions. An agent can receive a query, assess account history, resolve straightforward issues, escalate complex cases and update systems automatically, ensuring consistency and faster resolution.
Across these use cases, the benefits of agentic AI for finance are consistent and measurable.
Efficiency improves as agents take ownership of multi-step processes rather than individual tasks. Accuracy increases because decisions are informed by real-time data and consistent logic. Costs reduce as manual intervention and rework decline.
As a result, finance professionals are freed from operational oversight and can focus on strategic contribution. Agentic AI supports that transition by handling operational complexity in the background.
With greater autonomy comes greater responsibility. Agentic AI systems operate across sensitive financial data and critical processes, making governance essential.
Data quality remains foundational. Agents are only as effective as the information they rely on, so ensuring consistent, accurate data inputs is critical.
Security and access controls must be clearly defined. Agents should operate within strict permissions, with full auditability of actions and decisions.
Human oversight is also vital. While agents can act independently, accountability always sits with the organisation. Clear escalation paths, transparency and explainability help ensure trust in agentic systems.
Regulators are increasingly focused on AI governance, particularly in financial services. In the UK, the government’s pro-innovation approach to AI regulation focuses on five cross-cutting principles: safety, transparency and explainability, fairness, accountability and governance, and contestability and redress. For finance leaders, these principles reinforce the importance of implementing agentic AI with clear controls, documented decisions and defined accountability.
Looking ahead, agentic AI will become a core component of modern finance functions.
Routine oversight gives way to strategic insight. Manual coordination is replaced by intelligent orchestration. Finance teams move closer to the heart of business decision-making.
To prepare, organisations should start with practical steps:
Most importantly, keep the focus on people. Technology only delivers value when it supports how teams work and helps them achieve better outcomes.
Agentic AI for finance is already moving from concept to reality, helping finance teams make faster decisions, strengthen governance and spend more time on strategic work. The real opportunity for business leaders lies in adopting it thoughtfully, with clear objectives, strong oversight and a focus on supporting people, not replacing them.
If you’re exploring how agentic AI could fit within your finance function, having the right partner makes the difference. At BCN, the focus is on understanding what success looks like for your organisation, then helping you take practical, confident steps towards it. Often, it starts with a conversation. BCN supports finance teams in assessing readiness, defining appropriate use cases, and implementing agentic AI with the right balance of autonomy, governance and control.
What’s the difference between agentic AI and traditional finance automation?
Traditional automation follows predefined rules to complete individual tasks. Agentic AI for finance goes further by managing end-to-end processes, making decisions, adapting to change and working towards defined outcomes with minimal manual intervention. If you’re new to the concept, a good place to start is with the basics – what are AI Agents?
Is agentic AI suitable for regulated finance environments?
Yes, when implemented properly. Agentic AI can support compliance by operating within defined controls, maintaining audit trails and escalating decisions that require human oversight. Governance and transparency remain essential.
For a deeper look at how UK financial services organisations can adopt AI responsibly, explore BCN’s whitepaper – Balancing AI Innovation and Risk in UK Financial Services.
How should finance teams get started with agentic AI?
Most organisations begin by identifying complex, repetitive processes where delays or manual hand-offs create risk or inefficiency. Piloting agentic AI in controlled areas allows teams to understand the value, manage risk and build confidence before scaling.
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