Deepfake technology uses AI deep learning to generate highly realistic fake images and videos. As this technology advances, the barriers to creating and simulating realistic images and sounds have decreased, significantly increasing the potential risk of identity impersonation attacks. This poses a severe cybersecurity threat, particularly in the FinTech sector.
According to Gartner’s forecast, by 2026, 30% of companies will reassess the reliability of their identity verification solutions due to an increase in Deepfake attacks. Moreover, a survey by global forensic equipment and identity verification solution developer Regula indicates that 37% of companies have encountered Deepfake voice fraud, and 29% have fallen victim to Deepfake video fraud, showing an alarming rise in Deepfake identity fraud.
This article explores the impact of Deepfake technology on FinTech, presents relevant case studies, and provides strategies to counter these threats.
The Development of Deepfake Technology
What is Deepfake Technology?
Deepfake technology gained widespread attention in 2017. The term “Deepfake” combines “deep learning” and “fake.” The core principle is using AI to learn and generate realistic images or voices of a person and embedding this synthetic content into videos, making them appear as if they were genuinely recorded. Initially, this technology was used to superimpose celebrities’ or stars’ faces into videos.
How Does Deepfake Technology Work?
The core of Deepfake technology lies in its Generative Adversarial Networks (GANs), a system composed of two AI networks: a generator network and a discriminator network. The generator’s task is to create realistic images or videos, while the discriminator checks the authenticity of these images. If the discriminator identifies the image as fake, it provides feedback to the generator, which then adjusts to produce more realistic images.
What Threats Does FinTech Face from Deepfake?
Deepfake technology, which uses AI to generate realistic fake images and voices, poses unprecedented challenges to biometric technologies in the FinTech sector. This technology is widely used by criminals in various fraud activities, from creating fake videos of well-known tech personalities to promote fraudulent cryptocurrency transactions to using fake voiceprints for wire transfer scams. Deepfake technology has become a severe cybersecurity threat.
The fraudulent methods of Deepfake technology are diverse, ranging from impersonating high-level executives for phishing scams to creating fake social media profiles to spread false information. For example, a British design and engineering firm recently fell victim to a Deepfake scam, where an employee in Hong Kong was tricked into paying USD 25 million to fraudsters, causing significant financial losses.
Another example occurred in March 2019, when the CEO of a UK energy company received a call from a German parent company executive, instructing him to transfer €220,000 to a supplier in Hungary. The CEO recognized the caller’s “slight German accent and intonation” and completed the transfer within an hour. The funds were ultimately funneled into an illegal account in Mexico, and investigators later suggested that the thieves used Deepfake technology to mimic the German executive’s voice.
Earlier this February, Mugur Isarescu, the Governor of the Romanian Central Bank, was also targeted by a Deepfake attack. In a fake video, criminals used his image and voice to promote stock investments linked to a scam platform.
In response, the U.S. Treasury Department recently released a report on managing AI-specific cybersecurity risks in the financial services sector, aiming to ensure the safety and reliability of AI applications. The report highlights that AI is redefining cybersecurity and fraud prevention in financial services, urging the government to collaborate with financial institutions to use emerging technologies to maintain financial stability and continuously upgrade cybersecurity defenses.
What Strategies Can FinTech Companies Adopt?
As Deepfake technology advances, the concept of “seeing is believing” faces unprecedented challenges, severely threatening the trustworthiness of digital identity verification, and posing a significant risk to FinTech development. To maintain the integrity of the digital financial ecosystem, FinTech companies must adopt multi-layered strategies, encompassing technology, policy, and user behavior to ensure comprehensive protection.
For instance, companies can add unique hash values or security certificates to public videos to ensure the authenticity of the data source and prevent malicious tampering by hackers. This technical measure helps enhance the security of digital platforms and reduces the spread of fake content. Additionally, implementing multi-factor authentication (MFA) can further strengthen security, allowing consumers to gain authorization through multiple verification mechanisms, and reducing the risk posed by the misuse of Deepfake technology.
Moreover, the Zero Trust Architecture (ZTA) is a strategic cybersecurity model based on the principle of “Never Trust, Always Verify.” This model assumes that security threats may come from both external and internal sources, so neither external nor internal entities should be automatically trusted. Under this premise, the Zero Trust model requires continuous identity verification, access control, and security monitoring for all operations and access requests within an organization’s system to minimize security risks and prevent data breaches.
These strategies require significant investment in time, resources, and personnel training, and the establishment of comprehensive internal regulatory processes, with regular cybersecurity testing and drills. Additionally, combining consumer education programs to enhance user awareness and self-protection capabilities will help mitigate the threats posed by Deepfake technology, ultimately providing a safer operating environment for FinTech services.
Adopting AI Identity Verification Solutions to Safeguard Identity Security
As the threat of Deepfake technology to FinTech grows more severe, companies must take effective measures to protect the security of digital platforms and user data. Implementing Authme’s AI identity verification and anti-fraud technology allows quick and accurate identification of counterfeit documents, preventing fraud at its source and ensuring platform integrity. Additionally, Authme’s AI facial recognition technology, verified by ISO 30107 standards, effectively resists all types of spoofing attacks, including Deepfakes. This technology analyzes facial biometric features, such as facial depth, skin texture, and microvascular flow, ensuring the person in front of the camera is real, further strengthening the authenticity of user identities.
Through these proactive detection and prevention measures, companies can eliminate potential threats before fraudulent activities occur, reinforcing trust with users. To build a healthy digital financial ecosystem, companies and consumers must jointly address the challenges posed by Deepfake technology, creating a trusted service environment. By adopting Authme’s AI verification solutions, companies can better protect their digital platforms and user data, establishing a safer and more trustworthy digital financial environment.
