Scaling trust: how fraudsters use AI for social engineering

Artificial Intelligence is often seen as a force for good, powering innovation, boosting efficiency, and making digital experiences smarter. But the same tools that help banks modernise are also being used by fraudsters to scale deception.

AI hasn’t invented new types of fraud. What it has done is make old crimes faster, sharper, and harder to detect. Social engineering, in particular, has become more sophisticated; not because attackers are more creative, but because machines are doing the heavy lifting for them.

Social engineering is a form of psychological manipulation used by fraudsters to trick people into giving away confidential information, granting access to systems, or performing actions that compromise security. 

Rather than exploiting technical vulnerabilities, it targets human trust, using deception, urgency, or authority to influence behaviour. 

Traditionally, these attacks took the form of phishing emails or fake support calls, but as technology has evolved, so have the tactics.

In the past, social engineering relied on human effort. A scammer might craft a few convincing emails or make several calls, hoping to trick a customer into sharing credentials or authorising a transfer. Today, AI automates this process at a scale that would have been impossible a few years ago.

AI models can generate personalised phishing messages in seconds, tailor voice clones for smishing campaigns, and even create deepfake videos to impersonate trusted individuals. The result? Attacks that look and sound human, but can be launched thousands of times a day with minimal effort.

This shift means defending not just systems but also the trust customers place in the bank’s digital experience. It also helps recognise the signs of AI-enhanced social engineering early, ideally before a single account is compromised or any money moves.

Fraudsters don’t start with an attack; they start with reconnaissance. Before they ever send a message or attempt a login, they probe for weaknesses, analysing authentication flows, testing credentials, and mapping out how the platform responds to different behaviours.

AI supercharges this process. It can analyse login pages, session structures, and transaction sequences at machine speed, identifying potential gaps in authentication and session management that humans might miss. In the hands of an attacker, this means faster discovery, more targeted exploitation, and less time for banks to respond.

Worse still, Fraud-as-a-Service models now package these capabilities for anyone willing to pay. Pre-built AI-driven kits automate phishing, credential testing, and even session replay, allowing attackers with limited technical expertise to run complex, large-scale operations. What used to take days now takes minutes, and the attack surface grows accordingly.

Once attackers gain access to an account or device, AI allows them to act—and appear—like the rightful user. Using session replay and behavioural simulation, fraudsters can mimic legitimate customer actions, such as navigating interfaces, clicking buttons, and filling in forms, just as a genuine user would.

Traditional detection systems, which focus on static rules or transactional triggers, struggle here. The behaviour looks authentic because it’s been modelled on authentic data. AI enables attackers to fly under the radar, turning digital channels into a playground for undetectable fraud.

The good news is that banks can use AI to fight back, not by chasing every new attack, but by shifting left and detecting the early stages of fraud before it reaches the transaction layer.

Instead of focusing solely on identities and payments, banks can analyse what happens inside the session. Cleafy, for example, monitors clicks, form inputs, and flow sequences in real time, identifying patterns that suggest reconnaissance or rehearsal. A fraudster testing credential validity or rehearsing a transfer leaves subtle traces, and AI can spot them days, even up to 15 days, before the attack.

By detecting these preparatory actions early, banks can disrupt entire fraud campaigns before they reach customers’ accounts. The result: fewer false positives, lower operational pressure, and stronger protection of both trust and assets.

AI-driven social engineering demands an equally intelligent response. This means strengthening platform resilience by auditing authentication flows, monitoring session integrity, and ensuring the bank’s digital architecture can withstand automated probing and replay.

Moreover, it has become key to use session-layer visibility to see beyond the surface. Early signals of fraud - unusual navigation patterns, repeated form inputs, or suspicious flow sequences - often reveal the attack before it happens. When teams can act on those signals, they move from reacting to fraud to preventing it entirely.

AI isn’t changing what fraudsters do; it’s changing how fast and effectively they do it. As social engineering scales, so must trust. Banks that invest in detecting fraud before it matures—in the rehearsal, not the transaction—will gain a strategic advantage.

By combining AI-driven visibility with proactive detection, financial institutions can strengthen security and customer confidence. Because when trust is under attack, the only scalable defence is intelligence that sees further and acts faster.