## GenAI Fraud Detection

# Defend against deepfake fraud attacks

Battle-tested AI technology to detect and prevent fake identities synthetically-generated by AI.

Evasive AI has turned fraud into a mainstream threat, making traditional defenses dangerously obsolete.

Deepfakes are no longer niche; both organized networks and casual fraudsters use cheap tools to impersonate people, manipulate conversations, and erode digital trust. Costs are falling, quality is rising, and abuse is accelerating.

The real danger is blind spots. Most companies have little visibility into how exposed they already are, leaving them vulnerable to fraud, reputational damage, and regulatory scrutiny.

### Benefits  Microblink outpaces evolving GenAI attacks and deepfake fraud

Our proven, mature and innovative machine learning models detect synthetic ID documents and biometrics.

### Proprietary Synthetic Data

Our in-house AI team creates over 100,000 synthetic IDs monthly, building an unmatched proprietary dataset for red hat anti-deepfake training and testing. This scale and diversity gives our machine-learning models a decisive edge against evolving fraud tactics.

### Perfect Benchmark Results

Tested against the largest public, independent deepfake dataset backed by DHS, Microblink caught 100% of deepfakes with 0% false accepts. Performance held at a tolerance threshold aligned to an estimated FRR of 1.38–3.3%, setting a new standard for accuracy.

### Adaptive Universal Defense

The Microblink universal intelligence platform delivers multi-layered protection, dynamically adapting to new attack vectors. Designed for resilience, it future-proofs enterprises against both today’s mainstream deepfake exploitation and tomorrow’s unknown fraud threats.

## How To Measure Deepfake Detection

We pressure-tested Microblink against a US Department of Homeland Security-backed dataset.

## Our Solution: Fluid Digital Certainty

**Intelligence That Anticipates Threats –** Our multi-layered Fraud Lab replicates attacks and builds countermeasures, preventing fraud patterns before they emerge and strengthening with every breach attempt.

**Cross-Modal Learning Power –** Dynamic liveness detection across documents, biometrics, and cards ensures smarter, adaptive defenses against stolen identities, account takeovers, and GenAI-driven fraud.

**Proven Business Impact –** 97% customer satisfaction, NPS of 74, 40% capture rate improvement, and 50% fraud reduction—protecting revenue, reputation, and long-term trust.

### FAQ

### What is a deepfake in the context of fraud?

A deepfake is a synthetic piece of media, such as a video, photo, or audio recording, created using artificial intelligence to convincingly mimic a real person’s likeness or voice. Fraudsters use them to impersonate individuals and bypass identity verification systems.

### How can businesses prepare for evolving deepfake threats?

Enterprises should adopt multi-layered fraud defenses that combine document verification, biometric authentication, liveness detection, and AI-driven deepfake detection. Continuous model training against new AI fraud techniques is essential to stay ahead.

### Why are deepfakes a growing threat to businesses?

Generative AI tools make it easy and inexpensive for criminals to create highly realistic deepfakes. These are increasingly being used in account takeovers, fraudulent onboarding, and impersonation schemes, leading to significant financial and reputational risks.

### How can deepfakes be detected in real time?

Deepfake detection relies on advanced AI and machine learning models that analyze subtle inconsistencies in lighting, facial movement, frame transitions, and audio patterns. These signals are often imperceptible to the human eye.

### What types of algorithms are effective for deepfake detection?

A layered approach is most effective. Probabilistic visual algorithms uncover micro-errors in media, deterministic algorithms spot inconsistencies in personal information, and behavioral algorithms analyze anomalies in user actions during verification.