Artificial intelligence in finance: What UK finance teams need to know in 2026

Finance is among the earliest adopters of artificial intelligence (AI), and that momentum continues to grow. The Lloyds Financial Institutions Sentiment Survey found that 93% of financial services leaders believe AI and machine learning (ML) will have the biggest impact on the UK financial sector over the next five years, while 91% expect their organisation's investment in AI to increase over the next 12 months.

What started as pilot projects is now shaping core finance functions, from forecasting and risk management to compliance and operational efficiency. As AI evolves from a competitive advantage into an operational necessity, finance leaders need a clear strategy. This guide explores the key use cases, business benefits, and practical considerations for UK finance teams.

What is artificial intelligence in finance?

Artificial intelligence in finance refers to the use of machine learning, automation, and data analytics to streamline financial processes, reduce manual effort, and improve decision-making through real-time, data-driven insights. It enables finance teams to shift away from manual reporting and transactional data processing toward greater financial visibility, richer insights, faster analysis, and improved business performance.

The key AI sub-technologies shaping finance include:

Artificial intelligence: A technology that simulates human intelligence, enabling systems to interpret information, recognise patterns, and perform tasks that typically require human cognition.

Machine learning: A subset of AI that enables systems to learn from historical data, identify patterns, and generate predictions and other data-driven outputs.

Robotic Process Automation (RPA): Technology that uses predefined rules to automate repetitive, structured tasks such as data entry, account reconciliation, and report generation.

Natural Language Processing (NLP): A capability that enables machines to understand, interpret, and generate human language in text or speech form.

Predictive analytics: Uses historical data and statistical models to forecast future outcomes and trends.

Data visualisation: Presents complex datasets in visual formats such as dashboards and charts to make information easier to interpret.

Generative AI: AI capable of producing new content including text, images, audio, and code based on prompts and data inputs.

Agentic AI: An emerging form of AI capable of planning, reasoning, and executing multi-step workflows with minimal human intervention.

How is AI disrupting finance in 2026?

According to the Global AI in Financial Services Report by the Cambridge Centre for Alternative Finance, AI adoption is now widespread across financial services, with more than 80% of firms using it in some capacity. AI is reshaping how finance functions operate and is increasingly embedded in how teams process information, analyse performance, and support decision-making.

AI's greatest impact on finance lies in its ability to process data at scale, uncover patterns that humans may miss, and support real-time decision-making. By continuously analysing large volumes of financial and operational data, AI can surface insights far faster than traditional manual processes. It can identify anomalies, emerging risks, and trends early, helping organisations improve forecast accuracy, strengthen risk management, and respond more quickly to changing business conditions.

AI also enables more sophisticated scenario planning, allowing finance teams to model potential outcomes, evaluate different courses of action, and assess the financial implications of strategic decisions with greater confidence.

Key use cases of AI in finance teams

The transformation of finance is happening through a growing number of practical AI applications. The following use cases highlight where organisations are seeing the strongest results.

1. Accounts payable and invoice automation

As one of the most time-consuming and data-entry-intensive processes, accounts payable automation is one of the highest-impact use cases for AI. Automated systems can capture invoice data, match invoices to purchase orders, and route them for approval with minimal manual intervention. According to Ardent Partners' AP Metrics that Matter in 2025, best-in-class organisations using automation process invoices 82% faster. The result is lower processing costs, fewer errors, and greater visibility over cash flow.

2. Real-time financial reporting and close

Artificial intelligence helps automate core close processes, including journal entries, accruals, account reconciliations, variance analysis, and exception management. It also supports more continuous financial reporting by consolidating data across ERP, accounting, and operational systems, enabling ongoing reconciliations, and the preparation of up-to-date financial reports.

3. Budgeting, forecasting and scenario planning (FP&A)

Traditional FP&A cycles are constrained by slow data updates and manual model maintenance. AI reduces much of this friction by continuously processing financial and operational data and updating forecasting models as new information becomes available. This improves the quality and consistency of forecasting and budgeting, enabling more iterative planning cycles.

It also supports more robust scenario planning by allowing assumptions to be adjusted and tested without rebuilding underlying models. As a result, FP&A shifts from data preparation to understanding performance drivers and business outcomes.

4. Fraud detection and risk management

Machine learning continuously analyses transactional and behavioural data to identify anomalies, unusual patterns, and outliers that may indicate risk. It helps flag unusual transactions, detect deviations from expected behaviour, and prioritise high-risk activity for investigation. In addition, it enables continuous monitoring of exposure across credit, market, and operational risk, surfacing early indicators of emerging issues.

5. Regulatory compliance and Making Tax Digital (MTD)

AI supports compliance processes by applying rule-based checks and pattern detection to financial data, helping identify anomalies, inconsistencies, and potential regulatory issues. It strengthens audit readiness by improving traceability across financial records. It can help structure and validate data extracted from accounting systems to support digital tax submissions and reduce manual reporting effort.

6. Cash flow management and predictive analytics

AI analyses historical and operational data to identify patterns in cash inflows and outflows and support more accurate cash flow forecasting. It helps model payment behaviour and liquidity trends to anticipate future cash positions and highlight potential shortfalls or surpluses earlier in the cycle, enabling more proactive liquidity planning.

7. Spend management and procurement optimisation    

By analysing procurement and expense data at scale, AI improves visibility into spending patterns across categories, suppliers, and business units. This makes it easier to identify anomalies such as off-contract spending and unusual purchasing behaviour, reinforcing spend governance. Through analysis of supplier performance and pricing trends, it supports more informed sourcing decisions and improved procurement outcomes.

8. ESG reporting

AI supports ESG reporting by bringing together and analysing sustainability data from across finance, operations, and supply chains. It helps standardise how key metrics such as emissions, energy usage, and resource consumption are calculated and reported, while identifying gaps and inconsistencies before formal disclosures.

What are the benefits of AI in finance?

When applied across workflows, AI delivers meaningful business benefits, including:

1. Enhanced productivity and efficiency

Automating manual, repetitive processes, including data processing, reconciliation, and reporting significantly reduces time spent on transactional work. In organisations with embedded AI, finance professionals spend 20–30% less time on data analysis and preparation tasks. These gains can be realised across core finance operations, including accounts payable, financial close activities, reporting processes, and general ledger maintenance.

2. Lower operational costs

Automation reduces reliance on manual effort, lowering labour costs and costs associated with delays, rework, and exceptions. Improved accuracy and control reduce financial leakage and support more scalable finance operations.

Use the OneAdvanced Financials value calculator to estimate potential savings.

3. Improved accuracy and control

AI automates many manual finance processes, reducing human error and improving data integrity through consistent validation and discrepancy detection.

4. Informed decision-making

AI enhances finance decision-making by improving the timeliness and reliability of insights, enabling faster and more confident responses to business changes. KPMG research shows organisations report improvements in decision-making quality (70%), decision speed (71%), and forecast accuracy (64%) with AI adoption.

5. Scalability

It enables functions to scale operations as organisations grow, managing increasing transaction volumes, reporting demands, and business complexity without a proportional increase in resources.

Manual process vs AI-enabled process in finance

Area	Manual process	AI-enabled process
Data processing and reconciliation	Time-consuming, spreadsheet-based, prone to errors	Automated, continuous, and more consistent
Financial reporting and close	Periodic, reliant on manual consolidation	Faster, more continuous, and system-integrated
Forecasting and planning	Static models with limited updates	Dynamic models updated with new data inputs
Decision-making	Based on historical, delayed reporting	Driven by timely, data-backed insights

 

How will AI change the role of finance professionals?

While debates about AI's impact on jobs continue, its effect on the nature of work is already evident. As routine activities become increasingly automated, finance professionals spend less time on data processing and more time on analysis, stakeholder engagement, and business advisory. As a result, finance is becoming a more strategic partner to the business, with professionals playing a greater role in shaping decisions and driving performance.

This shift is changing the skills required within finance teams. Demand is growing for capabilities in data analysis, technology, critical thinking, and decision support, while a stronger understanding of AI and automation is becoming increasingly important. Rather than replacing professionals, AI is reshaping the role, creating opportunities to focus on higher-value work and contribute more directly to business outcomes.

AI and finance in the UK: the regulatory landscape

The UK has adopted a principles-based approach to AI governance rather than introducing a single AI-specific law. For finance teams, this means AI use is governed through existing regulatory frameworks covering financial services, data protection, operational resilience, and risk management. Key regulators include the Financial Conduct Authority (FCA), which oversees conduct and consumer protection in financial services, the Prudential Regulation Authority (PRA), which focuses on financial stability and risk management, and the Information Commissioner's Office (ICO), which regulates the use of personal data under UK GDPR.

Recent initiatives such as the FCA's AI Live Testing programme demonstrate the growing regulatory focus on the safe and responsible use of AI in financial services. Organisations deploying AI are expected to maintain appropriate governance, oversight, and accountability for AI-driven decisions while managing associated model and operational risks in line with existing regulatory expectations. Finance teams should ensure AI systems are supported by robust controls around data quality, transparency, human oversight, and compliance to meet regulatory requirements and build trust in AI-driven outcomes.

How to get started with AI in your finance function

Effective AI adoption starts with a clear plan. Focusing on high-impact opportunities first helps build momentum and demonstrate value. The following steps provide a practical framework for getting started.

• Identify high-impact processes for automation

Audit your current processes to identify where your team spends the most time on repetitive, rules-based work and where bottlenecks, delays, or inefficiencies occur. These are often the strongest opportunities for automation. High-volume activities such as accounts payable, reconciliations, and manual reporting are usually the best starting points.

• Identify one high-volume, rules-based process to automate

Select one rules-based process from identified opportunities to automate first. Focusing on a single use case helps demonstrate value quickly, reduce risk, and build confidence in AI adoption. High-volume processes such as accounts payable are ideal starting points as they are highly repeatable, easy to standardise, and typically deliver fast, measurable ROI when supported by modern financial management systems.

• Evaluate AI-enabled finance systems for automation

Evaluate solutions for automation, focusing on cloud-based finance software with embedded AI that supports automated workflows, real-time dashboards, and seamless integration. Prioritise platforms that are scalable and adaptable to future business needs.

• Implement change management and user adoption

Successful AI adoption depends on people as much as technology. Involve finance teams early, clearly communicate how roles will evolve, and address concerns around job impact. Provide training and support to build confidence in using new AI-enabled systems and ensure teams understand how automation will improve, rather than replace, their day-to-day work. Strong change management helps embed AI into everyday finance processes and drives long-term adoption.

• Measure and iterate for continuous improvement

Track adoption and performance outcomes to assess the impact of AI in finance. Establish clear metrics such as close cycle time, error rates, time saved, and improvements in reporting efficiency. Regular measurement ensures AI delivers measurable value and ROI, and supports scaling successful use cases across the finance function.

Customer spotlight: OneAdvanced Financials in action

Technology is increasingly delivering tangible value for finance teams across their day-to-day operations.

Nugent Care, the largest education, health, and social care charity in Liverpool, adopted our Financial Management software with a need to save time for their finance department:

“We were introduced to Financial's by our account manager at OneAdvanced and were impressed by how user-friendly the solution was. The product fits with our Cloud-first strategy and we love that it is a Software-as-a-Service model. From the price to the support and the scalability of the solution, we could see it was perfect for us.” - Julia Shaw, Head of Finance at Nugent Care

What stands out here is a clear expectation for financial management systems that combine capability with ease of use, scalability, and trusted partnership.

Build a future-ready finance function with OneAdvanced

While many organisations are already realising value from AI, there is still significant untapped potential across finance functions. The greatest value comes from embedding automation and AI into everyday processes, from accounts payable to reporting, forecasting, and planning.

Realising that value requires more than standalone AI tools. It depends on a platform that can connect data, automate workflows, and embed intelligence into day-to-day operations. OneAdvanced Financial Management is designed to support this, helping finance teams automate processes spanning accounts payable, reporting, forecasting, and compliance.

Powered by OneAdvanced IQ, the Intelligent System of Work the platform integrates financial data and workflows across business systems to reduce manual effort, improve visibility, and support better decision-making.

With robust controls, auditability, and support for evolving regulatory requirements, including Making Tax Digital, organisations can strengthen compliance while improving operational efficiency. Backed by decades of experience supporting UK organisations, OneAdvanced helps finance teams build a future-ready finance function.

The future of finance will be shaped by teams that can combine technology with strategic insight. Whether you are taking your first steps with automation or looking to scale existing initiatives, the opportunity to transform operations has never been greater.

Ready to unlock the potential of AI in finance?

Speak to one of our experts or book a demo to explore how OneAdvanced can support your finance transformation journey.

FAQs

How is AI used in finance and accounting?

AI is used across finance and accounting to automate repetitive processes, improve forecasting and decision-making, strengthen compliance, and increase operational efficiency. Common applications include invoice processing and accounts payable automation, real-time financial reporting, budgeting and scenario planning, fraud detection, cash flow forecasting, spend management, and ESG reporting.

Will AI replace finance and accounting jobs in the UK?

AI automates rules-based, repetitive tasks, but it does not replace the judgement, relationships, and strategic thinking that finance professionals bring. Rather than eliminating roles, AI is changing how finance teams work, enabling professionals to spend less time on manual processing and more time on analysis, stakeholder engagement, and business decision-making.

What is agentic AI and how will it affect UK finance teams?

Agentic AI refers to AI systems that can plan and execute multi-step tasks with limited human intervention. In finance, this could enable end-to-end, touchless workflows across areas such as financial close, variance analysis, compliance monitoring, and forecasting. While still emerging, it has the potential to reduce manual effort and improve efficiency, with human oversight remaining essential for control and decision-making.

Is AI in finance regulated in the UK?

Yes. AI in UK finance is regulated, but not through a dedicated AI law. Instead, it is governed by existing financial services regulation, UK GDPR, and operational resilience frameworks under the FCA, PRA, and ICO.

How secure is AI-powered financial management software for UK businesses?

AI-powered financial management software is generally secure when designed with strong controls covering encryption, access management, audit trails, and continuous monitoring. In the UK, security is further reinforced through compliance with UK GDPR and industry standards governing data protection and system resilience.

How does OneAdvanced use AI in its financial management software?

OneAdvanced’s financial management platform embeds AI across automated workflows, accounts payable and invoice processing, real-time reporting and dashboards, cash flow forecasting, and regulatory compliance. Through OneAdvanced IQ, its Intelligent System of Work, it integrates with existing ERP, HR, and operational systems to create a more connected and efficient finance function.

How can a CFO use AI to improve financial forecasting?

CFOs can use AI-powered FP&A tools to move beyond static, spreadsheet-based forecasts towards rolling, real-time models that draw on live transactional data, historical patterns, and relevant external signals. This enables more robust scenario analysis, allowing multiple potential outcomes to be modelled and presented to boards as probability-weighted forecasts rather than single-point estimates.