Deepfake AI technology is being used by criminals to conduct financial scams against businesses, with 53% of U.S. and U.K. businesses targeted and 43% falling victim to such attacks.
According to a survey by finance software provider Medius of 1,533 U.S. and U.K. finance professionals, 53% of businesses have been targets of financial scams powered by deepfake technology, with 43% falling victim to such attacks. Deepfakes are AI-manipulated images, videos, or audio recordings that are convincing but fake. In one notable case, British engineering group Arup lost $25 million after scammers used deepfake technology to pose as the organization's CFO during a video conference, leading an employee to make transactions to five Hong Kong bank accounts. Another example involved advertising group WPP being targeted by an unsuccessful deepfake scam. The scams typically involve criminals using online content like YouTube videos or podcasts featuring executives to create convincing deepfakes for impersonation scams, often creating false urgency to pressure employees into transferring company funds. Deloitte predicts that generative AI could enable fraud losses to reach $40 billion in the U.S. by 2027. The report notes a 393% increase in phishing attacks over the past year in finance and insurance sectors, with deepfakes being the second most frequent cybersecurity incident experienced by businesses in the last 12 months.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
Using AI systems to gain a personal advantage over others such as through cheating, fraud, scams, blackmail or targeted manipulation of beliefs or behavior. Examples include AI-facilitated plagiarism for research or education, impersonating a trusted or fake individual for illegitimate financial benefit, or creating humiliating or sexual imagery.
AI system
Due to a decision or action made by an AI system
Intentional
Due to an expected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed