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The latest serious issue facing finance: deepfake scams. They’re not just theoretical anymore—they’re happening, and deepfake statistics show they’re hitting finance hard.
Take the recent case in Hong Kong, where a finance worker transferred $39 million, thinking they were on a video call with their CFO and colleagues. Turns out, they were talking to deepfake impostors. This incident shows how deepfake tech is becoming a real threat in financial fraud.
But it’s not just about money. Deepfakes are also used to spread lies and create fake content that can harm reputations. With AI getting smarter, scammers can now mimic voices and trick people with convincing emails.
As finance leaders, it’s crucial to stay ahead. Regularly review security measures, train your teams to spot suspicious requests, and invest in tech that can catch deepfakes before they cause damage.
Join us as we dig into the latest deepfake statistics and ways to protect your company from these evolving cyber risks.
Only 15% state that they have never encountered a deepfake video.
Human subjects identified high-quality deepfake videos only 24.5% of the time.
A significant gap exists in educating students about identifying AI-generated content, with only 38% receiving guidance despite 71% expressing a need for such training.
Many students lack confidence in discerning AI-generated images, highlighting a critical need for improved media literacy education.
Available as open source code on GitHub, DeepFaceLab utilizes artificial neural networks to replicate visual and auditory features from an original video onto a target video.
Despite AI’s growing prominence, about one in four company leaders had little to no familiarity with deepfake technology.
31 percent of business leaders believe deepfakes have not increased their fraud risk.
32 percent of leaders had no confidence their employees would be able to recognize deepfake fraud attempts on their businesses.
More than half of leaders say their employees haven’t had any training on identifying or addressing deepfake attacks.
A further 10% were unsure if their enterprises had fallen victim to deepfake-based cyberattacks.
Last year saw a 10x increase in the number of deepfakes detected globally across all industries. This dramatic rise in deepfake detection underscores the rapid advancement of AI-powered fraud techniques and the urgent need for more sophisticated detection methods.
Crypto emerged as the main target sector for deepfake fraud, accounting for 88% of all deepfake cases detected in 2023. This statistic highlights the particular vulnerability of the cryptocurrency industry to advanced fraud techniques, likely due to its digital nature and potentially high financial stakes.
North America experienced a staggering 1740% increase in deepfake fraud. This enormous regional increase suggests that North America may be a primary target for deepfake fraudsters, possibly due to its large digital economy and high adoption of online services.
Fraud losses facilitated by generative AI technologies are predicted to escalate to US$40 billion in the United States by 2027. This projection reflects a compound annual growth rate of 32% from US$12.3 billion in 2023.
Incidents involving deepfakes in fintech surged by 700% in 2023, highlighting the rapid adoption of generative AI in perpetrating fraudulent activities.
Avast’s Q1 2024 Threat Report highlights a significant rise in cyberthreats exploiting human manipulation tactics like deepfakes and AI-manipulated audio synchronization. These threats are particularly rampant on platforms such as YouTube, where cybercriminals leverage deepfake videos and hijacked channels to disseminate fraudulent content.
Synthetic content technologies like deepfakes pose dual risks and benefits, complicating societal trust in media authenticity. While malicious uses, such as creating misleading audiovisuals, threaten public trust, they also offer innovative applications in fields like filmmaking and language translation.
Analysts have observed an increase in dark web conversations about using deepfake tools like DeepFaceLab and Avatarify to manipulate selfies or videos for identity verification bypass.
According to a 2024 survey, a significant 65% of Americans express concerns about potential privacy violations stemming from AI technologies. This apprehension reflects growing unease over the capability of AI to exploit personal data and invade privacy rights.
Threat actors increasingly use “face swap” deepfakes and virtual cameras to evade remote identity verification, reflecting a significant surge in attacks.
Gartner’s analysis indicates AI-generated deepfakes in face biometrics are prompting enterprises to adopt more robust approaches.
This finding underscores the growing prevalence of deepfake-related incidents in cybersecurity operations.
Conducted in 2023 across various industries, this survey highlights the widespread apprehension among business leaders regarding the risks posed by generative AI technologies.
Despite this awareness, only 29% of firms have taken steps to protect themselves against deepfake threats, with 46% lacking any mitigation plan.
This finding from a 2022 study indicates a significant gap between concern and proactive measures within the insurance industry.
A deepfake is a type of synthetic media created using artificial intelligence, typically altering videos or images to depict someone saying or doing something that never occurred.
Deepfakes use deep learning algorithms to analyze and manipulate existing images and videos. They employ techniques like generative adversarial networks (GANs) to generate highly realistic but fake media.
Deepfakes are used in cybercrime for various malicious purposes such as fraud, political manipulation, and blackmail. They can impersonate individuals to gain access to sensitive information or manipulate public opinion.
Look for signs such as unusual facial expressions, unnatural movements, low video quality, inconsistent audio, and asymmetries in the image or video. Asking specific questions or verifying with additional information can also help confirm authenticity.
To protect against deepfake scams, individuals and businesses should implement multi-factor authentication, regularly update passwords, keep devices updated, implement corporate controls like call-back procedures, and maintain a healthy skepticism by questioning and fact-checking all interactions.
Eftsure provides continuous control monitoring to protect your eft payments. Our multi-factor verification approach protects your organisation from financial loss due to cybercrime, fraud and error.