By Andrew Whittaker, Senior Business Consultant at SAS UK & Ireland
The fight against fraud in banking has never been more critical. As digital transactions continue to rise, fraudsters are evolving their tactics, exploiting vulnerabilities in legacy systems and in-house fraud controls.
Recent incidents at digital banks, like Revolut and Starling for example, have underscored the risks of inadequate fraud detection, with unchecked payments, account takeovers, and money mule activity becoming increasingly common.
These cases reveal a growing problem: traditional fraud prevention methods are struggling to keep pace with emerging threats, leaving financial institutions exposed to both financial losses and reputational damage.
The impact of regulatory changes
Adding to the urgency, regulatory shifts are changing the way fraud liability is handled. Under new rules, both the sending and receiving banks involved in transactions must now share the cost of reimbursing fraud victims, with a 50:50 split.
This marks a significant departure from previous models, where the burden often fell more heavily on one party. As a result, banks must rethink their fraud prevention strategies, ensuring they have robust controls in place for both outgoing and incoming transactions. This also calls for a more collaborative approach, to ensure fraud protection standards are improved industry wide.
The challenge is no longer just about detecting fraudulent transactions within a bank’s own systems. Institutions must also take responsibility for ensuring that they are not unwittingly facilitating fraud on behalf of bad actors.
This shift makes real-time analytics, AI-driven profiling, and centralised data integration essential components of an effective fraud prevention strategy.
The limitations of traditional fraud prevention
For many years, banks have relied on rule-based systems and manual transaction reviews to detect fraud. While these methods have had some success in identifying suspicious activity, they are proving inadequate against increasingly sophisticated fraud tactics.
Fraudsters are using social engineering, synthetic identities, and automated attacks to bypass conventional security measures. Because rule-based systems are static and rely on predefined fraud patterns, they often struggle to adapt to new and emerging threats.
Another major challenge is the fragmentation of data across different banking channels. Many institutions still operate in silos, making it difficult to gain a complete picture of customer activity. This lack of integration creates blind spots that fraudsters can exploit.
Without a holistic view of transactions and customer behaviour, banks are left reacting to fraud incidents after they occur, rather than proactively preventing them.
AI-driven fraud detection
To combat these challenges, banks must adopt AI-powered fraud detection systems, such as those powered by SAS Viya, which are capable of analysing vast amounts of transactional data in real time. AI and machine learning models can identify hidden patterns, detect anomalies, and flag potentially fraudulent transactions before they are completed.
Unlike traditional rule-based systems, AI-driven solutions continuously learn from new fraud patterns, allowing them to adapt dynamically to emerging threats.
Another key advantage of AI is its ability to integrate data from multiple banking channels, including online banking, mobile applications, ATMs, and in-branch transactions. By consolidating this data into a centralised system, AI-powered fraud detection can provide a more complete and accurate view of customer behaviour, reducing false positives and improving fraud detection rates.
Additionally, AI-driven behavioural biometrics, which analyse how users interact with their devices – such as typing speed, mouse movements, and login patterns – can help detect account compromise in real time.
Not just a regulatory requirement
Fraud prevention is no longer just a regulatory requirement, it is a strategic priority for banks looking to maintain customer trust and differentiate themselves in a competitive market.
Customers expect seamless and secure digital experiences, and any failure in fraud prevention can result in significant reputational damage. A single fraud incident can erode consumer confidence, leading to customer churn and long-term financial consequences.
Banks that invest in advanced AI fraud detection solutions not only protect their customers but also position themselves as industry leaders in security and innovation. By preventing fraud more effectively, these institutions can reduce financial losses, improve operational efficiency, and ensure compliance with evolving regulations.
Proactive fraud prevention
The adoption of AI-driven fraud prevention is no longer optional – it is essential for banks looking to stay ahead of increasingly sophisticated fraudsters and build a more resilient financial ecosystem.
The financial industry is facing an inflection point in the battle against fraud. With AI-powered solutions offering greater agility, accuracy, and scalability, banks have the opportunity to move from a reactive to a proactive fraud prevention model.
Now is the time to embrace intelligent fraud detection strategies, ensuring that financial institutions remain secure, compliant, and trusted in an increasingly complex digital world.
Andrew Whittaker, Senior Business Solutions Manager, SAS
Andrew Whittaker has worked in fraud prevention since 2011. In his role he provides subject matter expertise to drive the enhancement, pre-sales and business implementation of the SAS banking fraud solutions globally with both current and potential customers. Prior to joining SAS in 2022, Andrew spent over 10 years at HSBC where he held a variety of roles in fraud analytics combatting transactional fraud for cards, digital events and online payments.
