Make no mistake, it is an enormous concern how quickly and significantly AI is being innovated to assist in banking fraud. In particular, social engineering attacks to steal personal information, enable identity theft and unauthorised access to bank accounts, is becoming remarkably advanced.
Key areas of concern currently:
| Attack vector | Method | Risk |
|---|---|---|
| Phishing and social engineering | AI-generated phishing emails using natural language processing (NLP) to create highly convincing and personalised messages | Recipients tricked into revealing sensitive information |
| Deepfake technology | Realistic audio and video deepfakes impersonating bank officials or trusted individuals | Fraudulent transactions and unauthorised account access |
And where is it heading? Well the next phase is seeing further malicious innovation around enhanced social engineering.
Primarily this means behavioural insights around two key areas:
- Psychographic Profiling: AI will analyse vast amounts of data to create detailed psychographic profiles of individuals, tailoring social engineering attacks to exploit specific psychological traits.
- Manipulative Interactions: AI-powered chatbots and virtual assistants will engage in prolonged, convincing interactions with targets to extract sensitive information or induce them to perform actions that compromise their security.
This sophisticated AI fraud will inevitably mean that ID verification methods must evolve in particular around enhanced biometric authentication.
Key areas here are:
| Verification method | How it works |
|---|---|
| Multimodal biometrics | Multiple biometric factors used simultaneously to provide a strong verification process that is harder to spoof |
| Liveness detection | Advanced techniques to distinguish between real individuals and AI-generated deepfakes or other spoofing attempts |
| Behavioural biometrics | Monitoring user behaviour patterns (e.g., typing rhythm, mouse movements) to continuously verify identity throughout a session, rather than relying solely on initial login credentials |
| Context-aware verification | Analysing the context of user actions (e.g., location, device usage patterns) to identify anomalies that may indicate fraudulent activity |
And to ensure defence mechanisms evolve it is vital to collaborate with AI defence systems that can learn and adapt in real-time to detect and respond to emerging threats and machine learning models to identify and predict potential fraud based on evolving patterns and behaviours.
The Zenoo platform is focused on identity verification and security and plays a pivotal role in ensuring security around criminal AI use. To ensure this we partner with providers of the latest AI detection solutions which can be easily integrated into existing or new customer onboarding flows to combat fraud.
We MUST take advantage of those specialists who are analysing and responding to the AI threat on a daily basis.
