The Financial Conduct Authority (FCA) plays a central role in setting and enforcing standards for fraud prevention and financial crime management in the UK. For banks, insurers, and financial technology firms, the regulator’s expectations are clear: they must prove that they have strong systems in place to detect, prevent, and report suspicious activity. In practice, this requires more than traditional controls. It demands a shift towards modern fraud detection tools that combine automation, behavioural insight, and real-time monitoring to keep pace with the risks the FCA highlights.
Understanding FCA Requirements
The FCA’s Financial Crime Guide sets out what it expects from regulated firms. At its core, the guidance focuses on four elements: risk assessment, controls, monitoring, and reporting. Firms must identify the threats that are most relevant to their business, implement controls that are proportionate to those risks, and make sure those controls remain effective over time. Continuous monitoring is required so that suspicious activities are spotted quickly. Senior management are also expected to oversee fraud prevention and to demonstrate that they can act on accurate management information when risks emerge (FCA handbook).
This expectation is not static. In 2024 the FCA updated its requirements to strengthen focus on sanctions and proliferation finance risks, urging firms to refresh their risk assessments more frequently (FCA policy statement). Failures to keep up carry real consequences. Monzo was fined in 2025 because gaps in customer due diligence and onboarding left weaknesses in its financial crime controls (Financial Times). Starling Bank also faced penalties for deficiencies in its anti-money laundering and sanctions screening systems as it grew rapidly without updating its detection tools (Reuters).
These cases underline the FCA’s position: firms must match the sophistication of criminals with equally advanced detection systems or risk fines and reputational harm.
Modern Fraud Detection Techniques
Technology offers solutions that can transform compliance. Three approaches stand out as most effective in meeting FCA requirements.
Machine Learning and Artificial Intelligence
Machine learning systems process vast quantities of structured and unstructured data to identify suspicious patterns. Instead of relying on static rules, they adapt as new forms of fraud emerge. The Bank of England’s 2024 survey on AI in financial services found that three quarters of regulated firms already use AI in some form, with many applying it directly to fraud detection (Bank of England). This shows adoption is becoming standard practice rather than optional innovation.
Behavioural Analytics
Fraud is often revealed through small deviations in customer behaviour. Behavioural analytics helps institutions identify these signals. It looks at login patterns, transaction sequences, and changes in device or location usage. This approach reduces false positives and strengthens fraud prevention in areas like mule account activity and account takeover fraud. Academic research has confirmed the potential of behavioural analytics to improve accuracy and compliance standards (MDPI research paper).
Real Time Transaction Monitoring
The FCA has made clear that monitoring cannot be retrospective. Real time systems are required to flag suspicious payments as they happen. Visa and Pay.UK demonstrated the value of this approach with a pilot using AI to monitor account-to-account payments. The project increased fraud detection rates by 40 percent and identified fraud that had already passed through existing banking systems (Visa case study).
Together, these technologies enable firms to deliver the continuous monitoring and responsive reporting that the FCA expects.
Case Examples of Technology in Action
Practical outcomes show how these methods deliver compliance improvements.
- Visa and Pay.UK: By applying AI to account-to-account payments, they detected over half of the fraud that had slipped past other bank controls. The pilot projected potential prevention of £330 million in annual fraud losses. This is a clear example of regulators’ goals aligning with operational results, proving that AI can provide both compliance and customer protection.
- UK Retail Bank with GlobalLogic: A major bank adopted machine learning and operational dashboards to improve fraud monitoring across brands. The system tracked login anomalies and transaction irregularities, allowing compliance teams to intervene in real time. The initiative led to fewer fraud incidents, faster investigations, and stronger audit readiness (GlobalLogic case study).
These cases demonstrate that modern fraud detection is not an abstract concept. It is a proven capability delivering measurable outcomes.
Benefits of Adoption
The FCA focuses on compliance, but firms that adopt advanced fraud detection benefit in broader ways.
Reduced Regulatory Risk
By aligning systems with FCA guidance, firms lower their exposure to fines and investigations. The Monzo and Starling penalties show how costly gaps can be. Stronger monitoring reduces these risks and demonstrates to the regulator that the business takes compliance seriously.
Operational Efficiency
Automation reduces reliance on manual case review and allows compliance staff to focus on high-value investigations. It also shortens response times. Faster detection means that suspicious cases can be reported and escalated immediately, meeting FCA expectations for timely action.
Customer Trust
Fraud prevention is visible to customers. When clients feel their bank or insurer protects them effectively, trust improves. This in turn supports retention and growth. Reputational benefit can be as valuable as regulatory protection.
Stronger Audit and Oversight
Modern systems generate clear audit trails, scenario testing results, and management information dashboards. These tools help senior managers demonstrate that they understand fraud risks and can act decisively. That aligns with one of the FCA’s key requirements: that management has the insight needed to take responsibility for financial crime prevention.
How to Move Forward
Financial institutions should begin by reviewing their current systems against FCA requirements. Are risk assessments current and refreshed often? Do monitoring systems operate in real time? Can behaviour analytics detect suspicious deviations before losses occur? Do senior managers have access to clear data that allows them to discharge their duties?
Firms that answer no to any of these questions face both compliance and reputational risks. The way forward involves targeted investment in AI-enabled detection, behavioural analytics, and automated monitoring. These technologies do not need to replace existing systems entirely. Instead, they can be layered on to strengthen compliance without disrupting current operations.
At the same time, firms must maintain clear governance around these tools. The FCA has noted that automation brings responsibilities for transparency and explainability. Institutions must be able to show how their models work, how decisions are reached, and how false positives are managed. This ensures the regulator sees both innovation and accountability.
Bottom Line
Fraud is evolving and the FCA expects financial institutions to evolve with it. Compliance is no longer achievable with static rules and retrospective reviews. Instead, success depends on tools that adapt in real time, analyse behaviour patterns, and provide management with actionable intelligence.
Institutions that take this approach will find that compliance becomes less of a burden and more of an enabler. Modern fraud detection technologies strengthen operational efficiency, improve customer trust, and demonstrate to regulators that the firm is prepared to meet the highest standards.
The message is clear: the FCA is raising expectations, and firms must respond. Those that act now will reduce their exposure to penalties, build trust with customers, and achieve a stronger position in an increasingly demanding market.