How AI Can Protect Us from Digital Scams

Digital scams are becoming more credible, faster and above all more personalised. In the past, many phishing campaigns could be recognised by their grammatical errors, rough graphics or plainly generic messages. Today, a criminal can use artificial intelligence tools to write perfect emails, imitate the tone of an executive, generate credible websites, clone a voice or create a synthetic video. The quality of the deception increases while the cost of producing it decreases.

The good news is that the same AI can be used in defence. Modern anti-fraud systems no longer simply look for suspicious words or addresses on a blacklist. They analyse behaviours, relationships between accounts, payment anomalies, device characteristics and textual or audiovisual signals. In practice, they seek to recognise not only “what” is happening, but how consistent that situation is with the normal behaviour of a person or an organisation.

1. Why modern scams are harder to recognise

The strength of generative AI for a fraudster is scalability. The same criminal scheme can be automatically adapted to thousands of victims. It is enough to collect public information from social networks, company websites or data breaches to create communications that cite colleagues, clients, roles, recent events and real projects. The scam stops looking random and becomes contextual.

A second factor is multi-channel social engineering. The victim may first receive a message, then a phone call with a synthetic voice and finally a link to a portal that looks perfectly like the company one. Each element reinforces the previous one. In this scenario, relying exclusively on the user’s attention is insufficient: the defence must be distributed among people, processes and technology.

Finally, there is the problem of speed. Many frauds work because they push people to decide within a few minutes: “pay immediately”, “confirm the code”, “update the IBAN”, “the CEO is in a meeting and cannot call”. Defensive AI can be useful precisely because it evaluates signals that a human being under pressure cannot cross-reference in time.

2. Detecting anomalies: when behaviour is worth more than the password

One of the most mature fields is anomaly detection. A system can build a statistical profile of the normal activities of a user or an organisation: access schedules, location, devices, payment frequency, typical amounts, usual beneficiaries, browsing patterns and operational sequences. When a combination very distant from historical behaviour appears, the risk increases.

Let us imagine a purchasing manager who normally makes bank transfers within certain thresholds and only from a company device. If suddenly a new beneficiary is entered, the amount is high, the transaction takes place from a different network and a few minutes earlier the password was changed, no single signal proves a fraud. Taken together, however, they tell an anomalous story. An AI model can assign a risk score and request an additional check before authorising the payment.

This logic is particularly powerful because it does not depend on a static list of known scams. It can also intercept new schemes, provided they generate a significant deviation from normality. The limit, naturally, is the risk of false positives: blocking too many legitimate transactions reduces trust and productivity. This is why models must be calibrated and continuously monitored.

3. AI against phishing, deepfakes and impersonation

In phishing, language models and classifiers can evaluate characteristics that escape a traditional filter: artificial urgency, anomalous requests, contextual inconsistencies, similar but not identical domains, divergences between the declared identity and the technical infrastructure of the message. Protection improves when text analysis is combined with metadata, domain reputation and previous sender behaviour.

For deepfakes, the challenge is more complex. Detection systems look for visual artefacts, audio inconsistencies, compression patterns or biometric signals that are difficult to synthesise perfectly. But it is a continuous race: when the detector improves, the generators improve too. This is why the most robust strategy is not to ask only “is this video fake?”, but to verify the entire context of the request.

In a company, an urgent payment request should not be authorised simply because the voice sounds like that of the chief executive officer. It must be verified through a second channel, an approval procedure and information that an attacker cannot easily replicate. AI can flag the risk; the company process must prevent a single synthetic piece of content from becoming sufficient proof.

4. The best AI is the one embedded in a control process

A frequent mistake is to treat artificial intelligence as a new antivirus capable of solving everything on its own. In reality, the most effective anti-fraud systems work as a decision-support layer. They collect signals, estimate probabilities, prioritise suspicious cases and suggest checks. High-impact decisions remain governed by defined rules, roles and responsibilities.

For a company, this means integrating AI with segregation of duties, multiple approvals, verification of new beneficiaries, strong authentication, event logging and periodic training. It also means measuring the quality of the model: how many real frauds does it identify? How many false alarms does it generate? Which categories of users or transactions are penalised in an anomalous way? A system that is not verified can create a new risk while trying to reduce another one.

Another essential component is privacy. To detect anomalies, AI needs data, but the collection must be proportionate, secure and consistent with the regulatory framework. Fraud prevention does not justify indiscriminate surveillance. The objective is to use the minimum set of information necessary to recognise dangerous behaviours and document why an alert was generated.

Conclusion: defending means combining technology and judgement

Digital scams are becoming increasingly sophisticated precisely because they exploit automation, data and artificial intelligence. The response cannot simply be to ask people to “pay more attention”. A system is needed that recognises weak signals, cross-references data in real time and slows down decisions at high-risk moments.

AI can become an extraordinary prevention tool if it is embedded in a clear governance model. It does not replace human control, but can direct it where it is truly needed. For a risk-management-oriented organisation, this is the central point: transforming millions of digital events into a few understandable, verifiable and actionable signals before a fraud attempt becomes a concrete loss.