AI in Finance Malta: How systems and artificial intelligence support better financial decision-making

AI and modern finance systems are helping Maltese organisations improve financial reporting, forecasting and decision-making while enabling finance teams to focus on higher-value strategic activities.

For many years, the finance function’s core responsibility was accuracy: recording transactions correctly and producing reliable information on time.

Accuracy remains essential, but it is no longer where the function creates its greatest value. The tools available for entering, matching and checking financial information have improved considerably, while expectations of finance teams have grown even faster.

Finance professionals are now expected to interpret performance, identify emerging risks, support planning and provide timely insight to decision-makers. AI and modern finance systems can help meet these expectations, but only when they are supported by connected data, well-designed processes and professional judgement.

The value of AI in finance is not simply that it produces faster answers. It is that this speed gives finance teams more time to understand what those answers mean and decide what action should follow.

From processing data to interpreting it

Before automation became widely available, much of a finance team’s time was spent processing information.

Invoices were entered manually. Bank statements were reconciled line by line against the general ledger. Management reports could take days or weeks to compile, often arriving after the period they were intended to explain.

This did not reflect a lack of capability within finance teams. It reflected a lack of time and visibility. When most of the working day is spent entering and checking data, there is limited capacity to analyse what that information is saying. By the time a report reaches management, the figures may already be historical.

The first generation of finance technology addressed this by making existing processes faster. Bank feeds reduced manual imports, optical character recognition extracted information from invoices and rules-based workflows automated routine coding and approval decisions.

The objective was primarily efficiency.

Moving from automation to augmentation

The current wave of finance technology goes further. Modern platforms increasingly act as an interface between finance professionals and the information they need, rather than simply providing a faster way to process transactions.

Instead of navigating multiple systems, exporting reports and preparing commentary manually, a finance professional may be able to ask a direct question and receive an initial explanation, trend analysis or suggested next step.

The first wave of finance technology automated tasks. The next is helping finance teams think and respond faster.

This distinction matters. Automation removes manual work. Augmentation improves the quality and speed of the decision that follows it.

The organisations gaining the most value from AI are therefore not necessarily those automating the greatest number of tasks. They are those giving finance teams quicker access to reliable insight and more time to apply professional judgement.

A changing role for finance teams

As the processing burden decreases, expectations of finance teams continue to rise.

Finance professionals are increasingly expected to explain variances as soon as they appear, identify incorrect inputs before they influence decisions and provide cash flow guidance based on actual customer, supplier and contractual information.

Management teams also expect finance to answer questions using current reports rather than returning days later after a new spreadsheet has been prepared.

This changes the finance function’s role. It moves from recording and reporting past activity towards supporting decisions about future performance.

However, this evolution depends on more than introducing AI tools.

AI is only as reliable as the system beneath It

One of the most common misconceptions about AI in finance is that an organisation primarily needs a better model. More often, it needs better data and stronger processes.

Finance teams frequently operate across disconnected accounting systems, spreadsheets, approval tools, banking platforms and document repositories. Each holds part of the financial picture.

Introducing AI into this environment may accelerate the production of information, but it does not automatically improve its accuracy or completeness. A quick answer based on fragmented or unreliable data remains an unreliable answer.

The strongest results are achieved when organisations first establish a connected finance ecosystem. Accounting systems, reporting platforms, supporting documents, approvals and operational information should work from consistent and controlled data.

Once this foundation is in place, AI can move from simply generating answers to producing insights that finance professionals can review, challenge and use with greater confidence.

Financial information is becoming conversational

Historically, accessing financial information required someone to know where to find it, how to structure a report and which filters or calculations to apply.

That is beginning to change.

Accounting platforms are becoming increasingly integrated with the applications finance professionals already use. Xero, which RSM uses when supporting smarter finance operations, has introduced integrations connecting financial data with Anthropic’s Claude and Microsoft 365 Copilot.

These developments allow users to explore financial information and obtain business insights through natural language, while the accounting platform remains the underlying source of financial data.

A finance manager may, for example, explore why margins have changed, identify overdue customers, review expenditure trends or obtain an initial draft of management commentary without first navigating several reports and exports.

The significance is not that conversational tools replace financial analysis. It is that they reduce the time spent locating and preparing information before meaningful analysis can begin.

The finance professional remains responsible for assessing context, testing assumptions and determining whether the resulting insight is appropriate for the business.

Turning insight into forward-looking decisions

Faster access to information is only part of the opportunity. The next challenge is turning that information into decisions and actions.

Once finance teams spend less time gathering data, expectations naturally shift towards forecasting, planning and business guidance. Leadership teams rarely want to understand only what happened last month. They also need to know what may happen next and what action should be considered.

Modern reporting platforms such as Syft are helping finance teams transform accounting information into dashboards, management reports, forecasts and scenario models.

The benefit is not simply faster reporting. These tools allow finance teams to move beyond explaining historical performance and devote more time to discussing future outcomes.

Integrated forecasting across profit and loss, the balance sheet and cash flow can provide stakeholders with a clearer view of where the business may be heading. Scenario planning can also help management understand how different decisions or external developments could influence liquidity, profitability and financial resilience.

The conversation can then move from “What happened?” to “What should we do next?”

This is where the finance function can create its greatest strategic value.

Augmentation does not mean redundancy

AI and modern systems should not be viewed primarily as tools for reducing the size of a finance team.

Their greater value lies in helping finance professionals work at a higher level. By reducing manual processing, improving access to information and supporting analysis, technology gives teams more time to focus on judgement, communication and decision support.

Human oversight remains essential. AI-generated outputs may be incomplete, inaccurate or insufficiently sensitive to the commercial context. Finance professionals must still validate the information, challenge assumptions and consider factors that may not be visible in the underlying data.

Technology can expand the finance team’s capacity, but accountability for financial interpretation and advice remains with people.

No longer an advantage reserved for large enterprises

Perhaps the most significant change is not what finance technology can do, but who can access it.

A decade ago, many advanced finance capabilities were largely limited to bigger organisations. Sophisticated reporting required specialist business intelligence systems, forecasting models were often maintained in complex spreadsheets and automation depended on significant investment in software and internal expertise.

That distinction is gradually disappearing.

Growing businesses can now access real-time reporting, automated reconciliations, integrated approval processes, forecasting tools, conversational financial information and AI-assisted analysis through cloud-based platforms and subscription services.

These capabilities can often be adopted without substantial infrastructure investment or a large internal finance department.

For Maltese organisations, the question is therefore no longer simply whether the technology is available. The more important consideration is whether it has been selected appropriately, implemented effectively and embedded into day-to-day decision-making.

The competitive difference increasingly lies not in access to systems, but in how well organisations use them.

Creating a more valuable finance function

AI has not changed the fundamental purpose of finance: to provide an accurate, honest and timely view of the organisation’s financial position.

What has changed is how much of the underlying processing can be supported by systems and how much more time finance professionals can devote to interpretation, planning and advice.

The true measure of whether AI and finance systems are working is not the number of tasks they automate. It is whether they help the finance team ask better questions, produce more reliable insight and give decision-makers the confidence to act.

RSM Malta’s Digital Advisory and Outsourcing teams work together to help organisations assess their finance processes, strengthen their systems and introduce technology in a way that supports real operational needs. From improving the underlying finance infrastructure to providing ongoing accounting and advisory support, our approach combines technology, process design and professional expertise.

To explore how your organisation can build a more connected, efficient and insight-led finance function, contact RSM Malta.

Article written by Rachel Cauchi - Manager, Digital, Yue Zheng - Analyst, Digital and Janise Fava – Senior Manager, Outsourcing