Can Artificial Intelligence Stop the Next Wave of Online Fraud?

Online fraud is becoming more complex, and traditional systems are struggling to keep up. So, artificial intelligence is now being tested to close that gap by scanning large volumes of data, detecting unusual behaviour, and stopping threats before they spread. 

But the same tools can be used to deceive, not just defend. The challenge now is to understand how AI is being applied, where it’s proving effective, and where new risks are emerging.

AI Can Be Quite Useful in Cybercrime Prevention

Artificial intelligence is increasingly used to detect and limit cybercrime. It works by reviewing large volumes of activity and identifying patterns that fall outside normal behaviour. This allows threats to be flagged early, often before harm occurs. Over time, AI systems improve as they learn from confirmed incidents.

Banks rely on AI to monitor transactions in real time. When transfers appear inconsistent with an account’s history or location, systems can pause activity and prompt further checks. 

Another industry where wider use of AI can be effective is online gambling. Platforms analyse betting behaviour, account movement, and transaction timing to identify signs of abuse or financial manipulation. These tools help teams focus on high‑risk activity and act before issues spread.

Retail platforms apply similar methods. AI systems identify fake reviews, repeated account access attempts, and irregular purchasing patterns. This strengthens security, reduces fraud‑related costs, and maintains trust between sellers and buyers.

Detecting Fraud in Real Conditions

AI systems detect fraud by processing behaviour rather than relying on fixed rules. They analyse login patterns, device signals, transaction timing, and usage habits to establish what normal activity looks like.

Once that baseline is set, deviations become easier to spot. Machine learning models improve through exposure to confirmed fraud cases, adjusting their thresholds as tactics change.

In real environments, these systems rarely work alone. AI is often paired with tools like biometric verification or behavioural scoring. Together, they build a fuller picture of user activity across multiple sessions. When behaviour shifts in ways that don’t align with previous patterns, alerts are triggered early. This allows teams to step in before damage spreads.

But Criminals Can Also Use AI

The same technology that strengthens security can also be used to bypass it. Fraudsters now rely on AI to produce phishing messages that closely resemble real communication. Such messages often include personal details pulled from leaked data, making them harder to dismiss as obvious scams.

Voice synthesis has added another layer of risk. Scammers can recreate the sound of a known person and use it to pressure victims into quick decisions, often involving payments or access credentials. 

Visual deception has followed the same path. Deepfake videos are used to create a false sense of authority, whether through fake endorsements or fabricated announcements. In parallel, synthetic identities combine real and artificial data to pass verification systems. AI accelerates this process by generating variations that avoid detection.

Recognising these methods is critical. Defences need to evolve at the same speed and with the same flexibility as the threats they face.

AI Works Best When People Know How to Use It

AI can process more data than any team can, but it can’t think through the consequences. The most effective systems are those guided by people who understand what the technology is doing and when to question it. Without that human layer, even the best tools can make the wrong call.

Training matters. When teams are shown how AI makes decisions, they’re more confident in its use and better prepared to step in when something looks off. Clear roles, proper checks, and steady oversight are what keep the system working as intended.

The threat landscape is constantly changing. AI gives security teams a head start, but it’s people who keep things grounded. In the end, it’s not about choosing between human judgment and automation. It’s about building systems where both are in place, and neither is left on its own.