Q. How is the fraud landscape in iGaming changing, and what are the biggest threats operators should be preparing for?
 

Fraud itself is not necessarily becoming entirely new, but the way it is carried out is changing significantly. We are still dealing with familiar problems such as account takeover, identity abuse, payment fraud, bonus abuse and exploitation of weaknesses in products or processes.

What has changed is the speed and scale. Automation and AI allow fraudsters to execute existing methods faster, more cheaply and on a much larger scale. Synthetic identities, deepfakes and AI-generated documents are certainly important developments, but for me, the more immediate transformation is happening in execution rather than invention.

There is also an organizational challenge that deserves more attention. Technology is adapting quickly, while organizational structures and responsibilities often change much more slowly. As fraud becomes increasingly interconnected, the weakest point may not always be the technology itself, but the structure surrounding fraud prevention.

Q. Why are traditional fraud prevention methods no longer sufficient?
 

If we define traditional fraud prevention as mainly manual and reactive controls, then the limitations are already clear. Predictive models, advanced analytics and automation are increasingly necessary.

The role of the fraud specialist is also changing. Instead of simply reviewing individual alerts, fraud analysts increasingly need to understand patterns and relationships between different events.

Take an automated carding attack as an example. One system may identify compromised credentials, another may detect an unusual transaction, while another identifies suspicious relationships between accounts. Every individual control can work correctly, yet the organization may still fail to recognize the wider attack quickly enough.

The problem is therefore not always a lack of information. Sometimes the organization already has all the information it needs but fails to connect it.

That is why I would argue that the problem with traditional fraud prevention is not simply outdated tools. Its architecture is outdated.

Q. Does this mean fraud prevention needs to become part of a wider risk management strategy?
 

Yes, although I would still keep Risk Management broader than Fraud.

Fraudsters do not operate according to the organizational structure of the companies they attack. A single fraud scenario can move through several systems, processes and control functions almost immediately.

For that reason, Fraud is particularly well positioned to act as a horizontal intelligence layer. That does not mean Fraud should take ownership away from KYC, AML, Payments, Compliance or other functions. Their individual mandates should remain intact.

What needs to change is how intelligence moves between those functions.

The organizational boundaries can remain. The intelligence should not.

Q. How can better connections between onboarding, KYC and AML improve fraud detection?
 

Onboarding, KYC and AML provide a good example of why isolated controls are not always enough.

A fraudulent identity may pass one verification process. A transaction may appear reasonable when viewed independently. Activity may remain below a particular threshold. None of these observations necessarily tells us what is actually happening.

When these signals are connected, however, the picture can become very different.

This creates an important distinction between validating an individual event and understanding the player behind those events.

The goal should therefore not simply be to introduce more controls or generate more alerts. It should be to create continuity of intelligence, so that information generated at one stage of the player lifecycle remains relevant when risk is assessed somewhere else or at a later point.

Q. How important are payments when it comes to identifying fraud?
 

Payments are one of the most valuable sources of fraud intelligence because the movement of money can reveal what is actually happening.

Payment products naturally receive a great deal of operational attention. They need to be fast, reliable and convenient. Fraud can sometimes remain secondary until the financial consequences become visible, and by that point the damage can happen very quickly.

Many fraud signals tell us that something might be wrong. Following the money can provide stronger evidence of what is actually taking place.

Velocity is particularly important in exploit fraud. If someone discovers a weakness in a payment flow, product logic or promotional mechanism, automation can turn a relatively small vulnerability into significant financial exposure before traditional monitoring has time to understand it.

A sudden change in transaction or activity velocity can therefore be one of the earliest indications that something has fundamentally changed.

Q. How are device and behavioral signals changing the way operators detect suspicious activity?
 

One of the biggest changes in modern fraud prevention is that we increasingly need to look at what a player does, rather than relying only on what the player tells us about themselves.

Identity information remains essential, but device and behavioral intelligence give us another perspective. They allow us to understand how an identity actually behaves over time.

At the same time, individual signals should not be overestimated. A device by itself may tell us very little. The same applies to a new payment method, an unusual login or a change in betting behavior.

Their real value often appears when several signals are interpreted together and compared with what we already know about the player.

This creates another challenge: scale. A fraud analyst can potentially have hundreds of signals describing activity, relationships and behavioral changes. It is unrealistic to expect a human to continuously combine all of that information for every player.

Technology therefore needs to do more than generate additional alerts. It needs to provide context and help identify relationships between signals so analysts can understand when the overall behavior no longer makes sense.

Q. Why does fraud prevention need to continue throughout the entire player lifecycle?
 

Because passing onboarding and KYC only tells us something about the player at a particular point in time.

A legitimate player can successfully pass verification today and have their account compromised tomorrow. Nothing was necessarily wrong with the original verification; the risk changed because the person controlling the account changed.

Account purchasing creates a similar problem. The identity stored in the system may remain exactly the same while the reality behind that identity becomes completely different.

Registration, login, payment, gameplay and withdrawal should therefore not be treated as completely separate risk events. They are different observations of the same evolving player relationship.

This is why fraud prevention needs to move from point-in-time validation toward continuous risk assessment.

Q. What role will AI and automation play in the future of fraud prevention?
 

I would avoid making AI sound more mysterious than it needs to be.

The immediate reason automation matters is quite simple: humans cannot process information or make decisions at the speed and scale required by modern fraud prevention.

That does not mean fraud teams will disappear. In fact, in many organizations they are expanding.

At first, that may seem contradictory. But one explanation is that Fraud itself is expanding horizontally faster than automation can reduce the need for human involvement.

Growing fraud teams are therefore not necessarily evidence that automation is failing. They may instead show that the role of Fraud is changing faster than technology is replacing traditional tasks.

The basic division of work remains familiar: machines handle scale, while humans handle complexity.

What is changing is the scope. Both automated systems and fraud analysts increasingly need to understand a broader part of the player lifecycle and connect intelligence that was previously separated into different areas.

Q. How can operators balance stronger fraud prevention with a frictionless player experience?
 

The better we understand normal player behavior, the less often legitimate players should have to prove that they are legitimate.

For a genuine player, fraud prevention should ideally be almost invisible. Better risk intelligence should allow operators to apply friction selectively instead of treating everyone as potentially fraudulent.

In other words, friction itself should become risk-based.

There is, however, another challenge emerging. As automated attacks become more sophisticated, fraudulent behavior may become increasingly difficult to distinguish from legitimate behavior.

In some cases, behavior that appears almost too normal may itself become suspicious. Perfectly predictable navigation, timing or transaction patterns designed specifically to avoid detection could eventually become a signal.

The absence of anomalies may, in certain circumstances, become an anomaly itself.

Q. So, what does the future of fraud prevention ultimately look like?
 

I believe we have already crossed the point where automation, continuous monitoring and organizational change are no longer future concepts. They are already reshaping fraud prevention.

The industry is unlikely to return to fragmented and reactive legacy models.

The building blocks for a more integrated approach already exist, and solutions on the market are becoming increasingly connected.

I may be somewhat biased here, which is probably an occupational hazard for a Fraud Manager, but I naturally see Fraud as the connective tissue in this transformation. For that reason, I would not describe the future simply as an AI singularity. I would call it a Fraud Singularity.

By that, I mean more than continuous scoring or simply following the player throughout their lifecycle. I mean an integrated intelligence environment in which information from different domains continuously contributes to understanding the same player, while conclusions generated by Fraud also become intelligence for those domains.

For example, a successfully verified identity should no longer be considered sufficient assurance if Fraud later establishes that control of the account has changed.

The same principle should work in the other direction. Intelligence from Payments, AML, KYC, device analysis or behavioral monitoring can change the fraud assessment, while a fraud conclusion can influence how those functions interpret subsequent activity.

This does not require Fraud to absorb other functions. Their responsibilities and mandates can remain separate.

What needs to change is the intelligence flowing between them.

The technology is already here. The next step is not more intelligence, but connected intelligence.

 

 

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