As AI-powered fraud grows more advanced, financial institutions are increasingly expected to move beyond rule-based systems and embrace adaptive, real-time intelligence – or risk falling behind.
Carlos Santa Cruz, Chief Technology Officer, Lynx
How customers are protected from financial crime has undergone an incredible transformation over the past two decades. From basic password systems to biometric authentication, from manual transaction reviews to real-time monitoring, the progress has been nothing short of remarkable. The field has evolved beyond recognition, with completely different tools at the industry’s disposal.
Yet the shift occurring now overshadows every single previous evolution combined. Fraudsters have embraced Artificial Intelligence (AI) not just as a tool, but as a complete operational philosophy. They test, learn, and adapt quicker than they ever have before, and their success rates improve daily. As a result, fraud cases have risen by 16% year on year. By sharp contrast, many banks are still debating whether to implement AI versus rule-based systems. This result of hesitation is significant, with over £1bn stolen in the UK alone by fraudsters last year, highlighting the scale of the challenge facing the industry.
The criminal application of AI continues to shift the fraud landscape. Synthetic identity creation has become automated, with algorithms generating thousands of fake personas complete with social media histories and credit profiles. Social engineering attacks have become increasingly sophisticated, with fraudsters utilising AI to craft more convincing and personalised approaches. Today’s scams have become more organised and widespread.
While these changes have occurred, most financial institutions remain anchored to reactive, rule-based systems that flag suspicious activity only after patterns emerge. These legacy approaches treat fraud detection as a compliance exercise. Documenting yesterday is useful, but if institutions cannot predict what might happen tomorrow, sophisticated fraudsters gain an upper hand.
Self-learning intelligence can transform risk management
To successfully combat rising crime levels, the industry needs to first change the mindset around fraud. If institutions continue with the assumption that patterns repeat predictably, attacks will continue to rise. Organisations must embrace embedded AI systems that recognise that criminal behaviour evolves continuously. This means using detection tools which learn and adapt in real-time. This shift from static rules to dynamic intelligence represents the key difference between reactive detection and proactive protection.
A growing number of technologies are demonstrating the potential. Advanced AI platforms are analysing behavioural patterns across multiple customer touchpoints simultaneously, identifying subtle anomalies that indicate emerging threats. Rather than waiting for transactions to complete and then flagging suspicious activity, these systems support the decision-making process, enabling earlier detection and mitigation of potentially fraudulent activity. They examine spending patterns, communication behaviours, and device usage to create comprehensive risk profiles that evolve with each interaction.
These aren’t constrained to individuals either. AI systems can identify money mule networks, detect coordinated attack campaigns, and predict which customers face elevated risk based on external threat intelligence. Such an approach moves fraud prevention from a reactive discipline to a proactive strategy.
Compliance benefits increase significantly when AI systems operate continuously rather than periodically. Traditional approaches can require extensive manual review processes, creating congested bottlenecks that slow legitimate transactions while potentially missing sophisticated fraud attempts. Using AI can help to streamline compliance by automatically documenting decision-making processes, providing regulators with transparent audit trails, while not replacing the expert review and final judgment made by bank analysts, while reducing operational overheads.
The need for urgent adoption
The need for action cannot be overstated. Every day in which financial institutions delay implementing advanced AI capabilities represents lost ground in an escalating race. Criminals won’t wait for regulatory clarity or internal consensus and continue to exploit society’s most vulnerable members. The £1bn stolen from UK consumers in 2024 represents just the beginning of what’s possible when criminal AI operates unrestrained.
The reasons to act with urgency aren’t just financial. Financial institutions that embrace embedded AI gain significant advantages in customer experience, operational efficiency, and risk management. They can approve legitimate transactions faster while detecting threats more accurately. Customers experience less friction during normal activities while receiving better protection against sophisticated attacks. Better yet, each of these improvements compound over time, driving long-term sustainable growth. For fintech companies in particular, implementing robust controls is essential for not only protecting customers but gaining the trust of banking partners and successfully scaling their operations.
The cost of doing business is already high, but organisations do not need to accept escalating fraud losses as one of them. Modern AI-powered fraud prevention systems offer financial institutions the flexibility to align detection capabilities with their specific risk appetite. Rather than forcing organisations to adapt to rigid solutions, new technology can be calibrated to match institutional preference – whether prioritising minimal friction for customers or maximising fraud detection rates.
The technology exists. The challenges are significant. Financial institutions that recognise this moment’s importance and act decisively may help to define the next generation of fraud prevention.
