AI in the Ledger: Combating Fraud Before It Starts

Λογιστικά/Έλεγχος/Φορολογικά,⠀

Νομοθεσία, Νομική & Δικηγόροι,⠀

Χρηματοοικ.-Ασφαλιστικά-Τραπεζικά,⠀

AI in the Ledger: Combating Fraud Before It Starts

In 2023, an AI system used by a Nordic bank flagged a string of small, routine-looking supplier payments as suspicious, before any human accountant or auditor had raised an eyebrow. The trigger? A subtle change in the payment timing and vendor metadata that suggested an inside job. The alert ultimately led to the exposure of a six-figure internal fraud scheme. Financial fraud in the UK and EU remains a serious and growing threat, costing businesses billions each year. Traditional rule-based systems and manual audits often lag behind today’s increasingly tech-savvy fraudsters. But artificial intelligence is fast becoming a frontline defence, evolving from buzzword to business essential. This article investigates the fresh and often surprising ways AI is transforming fraud prevention in accounting and auditing, going far beyond duplicate invoice detection, and exploring cutting-edge tools reshaping compliance in a rapidly changing regulatory climate.

Why Fraud is Evolving Faster than Accountants

Fraudsters are no longer relying on crude tricks; they’re deploying deepfake invoices, synthetic identities, and coordinated cross-border scams that are increasingly hard to trace. In 2022, a Hong Kong finance worker was duped into transferring £20 million after joining a video call featuring AI-generated deepfakes of company executives, a glimpse into the disturbing future of financial deception. Meanwhile, traditional accounting tools, manual audits, spreadsheets and legacy rule-based software simply aren’t designed to flag these new threats rapidly or consistently enough. Fraud detection systems that rely on static rules miss subtle behavioural anomalies, like invoice timing patterns or metadata shifts. The reality is that many finance teams are running 2025 businesses on 2010 infrastructure. This innovation gap is what fraudsters are exploiting. As technology evolves, so too must the tools accountants use, or they risk fighting tomorrow’s fraud with yesterday’s defences and inevitably losing.

EXPLORE ALL TRAINING OPPORTUNITIES BY EIMF

Beyond Red Flags

Traditional fraud detection systems often rely on hard-coded rules, flagging, for example, any transaction over £10,000 or duplicate invoice numbers. But fraud rarely follows such predictable patterns. Artificial intelligence now plays a more nuanced role, using machine learning to analyse complex, real-time data and spot subtle irregularities that would slip past both human auditors and static systems.

Take French fintech firm Lydia, for example. Their fraud prevention model employs machine learning to assess user behaviour across thousands of variables, including geolocation, transaction frequency and device usage, flagging account takeover attempts before a fraudulent transaction is approved.

AI also now reviews unstructured data using natural language processing (NLP), scanning emails and vendor correspondence for signs of fraudulent intent; think sudden tone shifts or unusual urgency in payment requests. Meanwhile, PwC has begun piloting generative AI tools to summarise massive audit trails and flag inconsistencies in financial narrative reports. This shift marks a departure from retrospective alerts to predictive, behaviour-driven insights. In other words, AI isn’t just raising red flags, it’s rewriting the fraud detection rulebook.

Predicting Risk, Not Just Reacting

Fraud prevention is no longer just about spotting what went wrong — it’s about predicting what could go wrong. With the rise of predictive analytics, AI is enabling accounting teams to detect fraud before any money actually leaves the business. By analysing both historical data and live transaction streams, modern AI systems can flag anomalies in real time, allowing firms to act within seconds rather than after a quarterly audit.

HSBC, for instance, uses machine learning to monitor millions of daily transactions and generate real-time fraud alerts based on shifting behavioural patterns, such as deviations in spending location, time or amount. AI-driven behavioural profiling can also catch internal threats. If an employee suddenly logs in outside normal hours or alters invoice details in unusual ways, the system learns these deviations and can escalate the alert.

Crucially, these systems learn continuously. They adapt to evolving fraud tactics, growing more accurate over time. This shift from reactive defence to predictive foresight marks a fundamental change in how accounting teams protect the ledger, stopping fraud before it has a chance to succeed.

EXPLORE ALL TRAINING OPPORTUNITIES BY EIMF

The Regulatory Dimension

As artificial intelligence becomes integral to fraud detection in accounting, regulatory frameworks in the UK and EU are evolving to balance innovation with oversight.

The EU’s Artificial Intelligence Act (AI Act) classifies AI systems based on risk levels. High-risk applications, such as those used in fraud detection, must adhere to stringent requirements, including transparency, human oversight and robust data governance. Non-compliance can result in fines up to €35 million or 7% of global turnover, whichever is higher.

In contrast, the UK’s approach post-Brexit is more principles-based. The Financial Conduct Authority (FCA) emphasises five key principles: safety, transparency, fairness, accountability and contestability. While not prescriptive, these principles guide firms in the responsible deployment of AI.

A 2024 FCA survey revealed that 75% of UK financial firms are using AI, but only 34% fully understand how it operates. This underscores the importance of explainability in AI systems, especially when decisions impact financial integrity. Firms like ComplyAdvantage are leading by example in this context, implementing explainable AI models that align with regulatory expectations, ensuring transparency in fraud detection processes.

Navigating these regulatory landscapes requires a proactive approach, ensuring AI tools not only enhance fraud prevention but also comply with evolving standards.

EXPLORE ALL TRAINING OPPORTUNITIES BY EIMF

What Leading Companies are Doing

Leading companies are redefining fraud prevention by integrating AI across their financial operations, moving beyond traditional audit-centric approaches. Instead of relying solely on post-transaction reviews, these firms embed AI into every layer of their financial systems, enabling real-time anomaly detection and proactive risk management.

Alongside this a key strategy approach involves forming cross-functional AI governance teams that bring together experts from finance, compliance, legal and IT departments. This collaborative approach ensures that AI initiatives align with regulatory requirements and ethical standards. According to OneAdvanced, establishing such teams is crucial for effective AI compliance frameworks, especially when they are functioning in real-time.

To enhance data analysis, companies are also adopting decentralised data lakes, allowing for the aggregation of structured and unstructured data from various departments. This holistic data integration facilitates more comprehensive fraud detection. Databricks highlights how their Lakehouse Platform enables real-time fraud detection by combining the best elements of data lakes and data warehouses.

In a further development, the use of synthetic data has become instrumental in training AI models without compromising privacy. J.P. Morgan, for instance, generates synthetic datasets to accelerate research and model development in financial services, improving fraud detection capabilities while safeguarding sensitive information.

By embedding AI throughout their operations, fostering cross-functional collaboration, leveraging decentralised data architectures, and utilising synthetic data, leading companies are certainly setting new standards in proactive fraud prevention.

The Road Ahead

AI won’t replace auditors and accountants, but it will empower them to work smarter, faster and with greater foresight. As AI models become more transparent and predictive, their use in fraud detection is likely to shift from optional innovation to regulatory expectation. For businesses, particularly SMEs, now is the time to explore how AI can be integrated into fraud prevention strategies in a compliant, explainable way. In a world of increasingly complex and invisible financial crime, slow and reactive defences are no longer fit for purpose. The tools are already here; the question i are you ready to adopt them?

Related Training Programmes