Banks are strengthening security across their digital channels, yet fraudsters are increasingly targeting customers rather than banking systems. Drawing on insights from Gökhan Dumrul, Head of Fraud Risk Management at QNB Türkiye, we examine how fraud schemes are evolving, why traditional transaction monitoring is no longer enough, and how banks can strengthen security without compromising the customer experience.
Digital fraud is increasingly more than an isolated incident. It is often the first stage in a longer chain designed to move and launder illicit funds. For banks and fintech companies, this changes the approach to risk management: anti-fraud, AML, cybersecurity and customer operations need to work as an interconnected system.
When a bank processes 1 billion digital transactions a year, fraud can no longer be treated as a security issue to be investigated after the event. Fraud losses cost money, but excessive blocking does too. The experience of Lasha Mazanashvili shows why tackling digital fraud depends not only on technology, but also on the speed of scenario development, data quality and visibility into what is happening on the customer’s device.
AI has not created fundamentally new forms of fraud, but it has transformed the economics of attacks. Schemes that once required large teams and substantial resources can now be automated and scaled at speed. Businesses must therefore rethink how they detect risk, respond to attacks and contain incidents before they spread.
Fraud schemes often evolve faster than internal controls. A mature anti-fraud function does more than investigate losses. It identifies weaknesses early, informs product design, connects signals across accounts and measures whether controls are reducing risk without creating unnecessary friction.
Why the most dangerous threat to businesses is no longer external hacking, but access gained through legitimate employee accounts. How security logic is evolving in hybrid environments, how attackers penetrate internal systems, and which signals can reveal an attack before it results in data leakage or infrastructure takeover.
How banks can build effective anti-fraud systems when resources are insufficient for total control. Why, in such conditions, the number of checks matters less than precise prioritization, the ability to separate noise from real risk, and the discipline to identify where fraudulent activity converges. What measures strengthen protection without heavy infrastructure: monitoring dormant accounts, limiting remote onboarding, analyzing links between transactions, and involving employees beyond the anti-fraud function.
Modern fraud rarely looks like a conventional cyberattack. It is often disguised as routine customer activity and moves through marketplaces, messaging apps, remote identity checks and gaps in a company’s own processes. What does this mean for anti-fraud, the allocation of responsibility and the way businesses manage risk?
AI in anti-fraud is often treated as a universal solution. In practice, however, the term covers fundamentally different tasks - from machine learning models built on large datasets to text structuring and anomaly detection. Igor Ermak explains why AI delivers the greatest practical value in detection, how teams work with confirmed fraud patterns and typologies, and where the limits of the technology begin.
How can companies fight fraud without killing conversion and sales? An industry expert shares hands-on experience—from basic rules to advanced models, from manual checks to resilient anti-fraud systems that not only reduce risk but actively improve customer experience and revenue.
