Modulate Partners with Scam.ai for Voice Fraud Detection

Modulate, a conversational voice intelligence company, and Scam.ai, a provider of deepfake and synthetic media detection technology, have partnered to bring Modulate's synthetic voice detection models directly into the Scam.ai platform.

Scam.ai has established deepfake detection capabilities across images, videos, and digital documents. By integrating Modulate's specialized synthetic voice detection models, Scam.ai will enable customers to expose the three primary forms of synthetic media—image, video, and audio—through one unified platform and workflow.

The integration addresses a growing challenge for organizations as deepfake attacks expand beyond a single format or communication channel. A fraudulent interaction might begin with a cloned voice over the phone, move to a fabricated image or document, and conclude with a manipulated video or identity-verification attempt. Defenses that examine only one component of the interaction risk missing the broader attack.

"Scammers stopped limiting themselves to one channel a long time ago, but many detection systems are still organized around individual media formats," said Ben (Simiao) Ren, co-founder and CEO of Scam.ai, in a statement. "By integrating Modulate's industry-leading models into Scam.ai, we are giving our customers a practical way to add synthetic voice detection to the platform and workflows they already use to analyze visual content. This gives fraud and security teams a more complete view of the interactions they are evaluating without deploying another standalone tool."

Through the partnership, Scam.ai will offer Modulate’s synthetic voice detection models as part of its own product portfolio and customer experience. Customers will be able to analyze live or prerecorded audio alongside images and videos, receiving confidence scores and detection signals that indicate whether a voice is synthetic or AI-generated.

"Deepfake attacks do not distinguish the boundaries between audio, images and video, and the technology used to stop them can't afford to either," said Carter Huffman, co-founder and chief technology officer of Modulate, in a statement. "Voice is increasingly part of coordinated multimedia deepfake scams. A convincing cloned voice can establish urgency and trust, while a fabricated video, image, or document reinforces the deception. Scam.ai understands that organizations need to evaluate the entire interaction, and this partnership puts voice detection directly into the platform across workflows their customers already use."

"People are being asked to determine whether a voice, image or video is authentic at the exact moment a scammer is trying to manipulate them," Huffman added. "That is an adversarial problem, and detection cannot depend on whether someone thinks a voice sounds suspicious. Organizations need automated systems that can analyze synthetic-media signals, explain why content was flagged, and help people make better decisions before money, access or sensitive information changes hands."

Modulate's synthetic voice detection technology supports real-time streaming and prerecorded audio. The model reports 98.9 percent accuracy and a 1.1 percent equal error rate. It returns confidence scores and detailed detection signals through the Modulate API built for integration into enterprise platforms and applications.

Scam.ai's platform currently provides real-time analysis of AI-generated and manipulated images and videos, with its Eva-v1 models reporting 98.2 percent visual detection accuracy against Scam.ai’s internal benchmark. The platform is designed to return confidence scores and manipulation analysis through a single API and customer interface.

Together, Modulate and Scam.ai will enable customers to:

• Detect synthetic and manipulated content across image, video, and voice through one unified platform and seamless user experience.
• Add synthetic voice detection to existing fraud prevention, authentication, and content-verification workflows.
• Use confidence scores and detection signals to prioritize high-risk content for additional review.

Potential applications include identity verification and digital onboarding, financial fraud and payment authorization, executive and employee impersonation, contact center security, social media and user-generated content moderation, insurance claims, digital evidence verification, and enterprise cybersecurity investigations.