Skip to main content
The Signicat Blog
Illustration featuring an ID document and a shield
Thais Guillen

Content Manager

AI-Driven Identity Fraud: How to Detect and Prevent AI Identity Fraud in Real Time?

AI-driven identity fraud now makes up 42.5% of detected attempts, making these sophisticated scams more scalable and effective with significantly less effort. From synthetic identities to other advanced attacks, these techniques are targeting companies across industries, leading to financial losses and reputational harm. Yet, the very technology fuelling AI Identity Fraud also offers a way to fight back. But how?

How AI Fuels Identity Fraud 

The same features that make AI revolutionary—speed, scalability, and adaptability—are increasingly being weaponised by fraudsters. If you’re wondering how to detect AI-driven identity fraud in real time, it’s essential to understand the techniques powering these attacks: 

  • Machine Learning Algorithms predict patterns in user behaviour to mimic legitimate actions, like access attempts or transactions. 
  • Natural Language Processing (NLP) models produce highly convincing phishing emails and bogus interactions, contributing to threats on a massive scale, like social engineering attacks among others. 
  • Generative Adversarial Networks (GANs) create realistic fake identities, complete with AI-generated images or video, making AI identity fraud detection more challenging than ever. 
  • AI-Powered Credential-Stuffing Tools automate logins at scale, bypassing brute-force prevention measures. 

These techniques illustrate why AI-driven fraud detection technology is crucial for modern organisations. Without robust AI fraud prevention strategies, it’s only a matter of time before your business becomes a prime target.

The Growing Cost of AI-Enhanced Scams 

Fraud losses across industries are staggering, with global payment fraud alone costing businesses an estimated £30 billion last year, according to the Nilson Report. The rise of AI-driven identity fraud amplifies these losses, thanks to higher success rates and widespread operational disruption.

–

"Deepfake fraud attempts have surged by 2,137% in three years."

Key Metrics to Consider: 

  • Fraud Prevalence: An estimated 42.5% of detected fraud attempts now involve AI-based fraud. 
  • Deepfakes: Nearly 6.5% of identity fraud attempts use deepfake technology, a startling increase of 2,137% over three years. 
  • Success Rates: Around 29% of AI-driven fraud attempts succeed, resulting in hefty revenue losses and damage to customer trust. 
  • Emerging Risks: Adversarial attacks on AI systems (attempts to trick AI models with manipulated inputs, causing them to make errors) and biometric spoofing (uses fake biological traits —fake fingerprints, facial images, or voice cloning—to trick security systems) are becoming more frequent, further complicating AI fraud prevention strategies.

Types of AI-Driven Identity Fraud in Financial Institutions 

Fraudsters are no longer limited to simple impersonation or document forgery. They now take advantage of AI fraud detection algorithms against institutions, creating new layers of complexity. Here are some of the most common tactics: 

Deepfakes 

Using AI-powered identity theft methods, fraudsters create hyper-realistic video or audio impersonations of genuine customers or high-level executives. These deepfake scams are particularly troubling in video-based Know Your Customer (KYC) processes, especially where KYC compliance challenges and AI solutions for fraud prevention haven’t yet been adopted. Alarmingly, deepfake fraud attempts have surged by 2,137% in three years, now accounting for 6.5% of all identity fraud cases. 

Synthetic Identity Fraud 

Combining real personal data (e.g., National Insurance Numbers) with fictional details, criminals craft convincing—but entirely fake—identities. AI speeds up this process by generating authentic-looking ID cards or utility bills, making synthetic identity fraud harder to spot. 

Account Takeover (ATO) Fraud 

Account takeover (ATO) fraud is increasingly common. Automated credential-stuffing attacks rely on AI fraud systems to test stolen credentials across multiple platforms until they find a match. Once inside, fraudsters can transfer funds, steal personal data, or conduct further scams. 

Document Forgery 

With AI identity fraud detection software still in its infancy at many organisations, forged passports, driving licences, and utility bills produced by AI-powered fraud tools can fool even well-trained staff. 

Social Engineering at Scale 

AI-powered fraud detection is vital as language models can create highly personalised phishing emails or messages. These targeted tactics cause costly data breaches, unauthorised payments, and reputational damage. 

From identity access management (IAM) solutions to a zero-trust security model to protecting against AI-based identity attacks, businesses must act decisively to avoid financial and reputational damage.

Preparing for the AI Fraud Boom 

Our latest findings, highlighted in the report “The Battle Against AI-Driven Identity Fraud” reveal that:

  • Over three-quarters of fraud decision-makers say AI-driven identity fraud is far more threatening today than it was three years ago. 
  • Only a quarter of these businesses have implemented dedicated measures to handle AI in fraud detection. 
  • Budget constraints, time pressures, and limited expertise remain key hurdles to deploying AI fraud detection software and other specialised solutions. 
–

“Fraud is likely to become more successful, but even if it doesn’t, the sheer volume of AI-driven attempts means fraud levels are set to explode.” 

Fighting AI with AI: How to Detect AI-Driven Identity Fraud in Real-Time 

While AI has enabled criminals to launch more sophisticated scams, it’s also paving the way for advanced capabilities in AI identity fraud prevention. Improving identity verification solutions with machine learning fraud detection can help identify and stop suspicious activity before it does serious harm. Below are some ways forward: 

AI-Powered Identity Verification 

Using AI fraud detection algorithms to confirm liveness and detect deepfake images or synthetic profiles is essential in combating AI identity fraud detection challenges. For example, platforms like Signicat’s VideoID rely on AI fraud detection tools to pinpoint anomalies—such as biometric spoofing—during onboarding.

Behavioural Analytics 

Machine learning fraud detection systems can assess keystroke speed, device settings, and session duration to highlight unusual activity. These insights spot anomalies far beyond human capability, strengthening your AI fraud prevention approach. 

Risk Orchestration 

Solutions like Signicat’s RiskFlow Orchestration bring together multiple data points—geolocation, device profiling, transaction velocity—into a single AI fraud detection system. Combining intelligence helps organisations respond more effectively to AI-powered identity theft attempts. 

Layered Security 

A layered approach is far more robust than relying on any single defence. Combining eID solutions, identity access management (IAM) measures and real-time AI-powered fraud detection ensures criminals face multiple hurdles, reducing the odds of successful account takeover (ATO) fraud. 

Continuous Monitoring 

Post-onboarding, social engineering attacks can still occur. Ongoing AI-driven fraud detection flags anomalies—like unusual login times or transaction sizes—helping you detect voice cloning scams and other emerging threats.  

Future Trends and Challenges in AI-driven Identity Fraud 

AI-driven identity fraud is constantly evolving, racing financial institutions to keep up. Here’s what to watch: 

  • Real-Time Deepfake Generation: Highly convincing deepfakes will bypass human scrutiny, highlighting the need for AI identity fraud detection software. 
  • Cross-Border Attacks: Fraudsters will exploit regulations across regions, making KYC compliance technology and AI solutions for fraud prevention a global priority. 
  • Automated Fraud Campaigns: Adversarial attacks on AI systems will become faster and more targeted, requiring stronger defences, such as the zero trust security approach. 

Don’t Let AI Fraud Take You by Surprise 

Our research shows that 76% of decision-makers view AI as a major driver of identity fraud. Yet only 22% of organisations have implemented AI fraud detection software, while most plan on deploying measures within the next 12 months.  

This gap reveals an alarming lack of urgency given that AI-driven identity fraud isn’t a future issue—it’s already here. Businesses must act decisively to protect their operations and customers from these threats. 

Steps for Organisations to Fight AI-Based Fraud 

  • Invest in AI-driven Defence Systems: Equip your team with advanced AI-powered fraud detection tools to tackle the different types of AI-Driven identity fraud, from biometric spoofing to synthetic identity fraud. 
  • Educate Your Workforce: Arm your staff against deepfakes, spoofing, social engineering attacks and account takeover (ATO) fraud, teaching them to recognise the signs of voice cloning or adversarial attacks on AI systems. 
  • Adopt a Zero Trust Security Model: Embrace a zero-trust security model, protecting against AI-based identity attacks by validating every user, device, and transaction—no exceptions. 
  • Partner with Trusted Experts: Signicat’s advanced solutions simplify improving identity verification solutions with machine learning fraud detection, helping you orchestrate multi-layered security protocols and protect your customers at every step.

By proactively embracing AI fraud prevention technologies, businesses can not only protect their reputation but also maintain the trust that underpins every successful financial service.