Unraveling the Deception: The Rapid Emergence of Deepfakes and Ways to Guard Your Company Against Manipulated Visual Content
In the ever-evolving world of technology, the finance and banking sector has found itself at the forefront of a new challenge: deepfakes. These highly realistic and convincing synthetic audio and video forgeries, enabled by Generative Adversarial Networks (GANs), have become a significant concern in the industry.
From 2022 to 2023, deepfake incidents in the finance sector saw a significant rise. This surge was exemplified by the $25 million loss at the global engineering firm Arup, where scammers used a real-time deepfake of the CFO during a video call to authorize fraudulent transfers. Deepfake face swap frauds on ID verification surged by 704% in 2023, demonstrating the increasing use of GAN-based deepfakes to bypass authentication systems relevant in financial services.
These GAN-driven deepfakes have transcended simple impersonations to mimic voices, faces, and gestures convincingly enough to manipulate employees and executives in real-time meetings, exploiting trust and procedural vulnerabilities. As a result, financial institutions face growing challenges, leading to predictions of massive fraud losses—up to $40 billion in the U.S. alone by 2027.
Identity theft, account takeover fraud, and reputational risk are among the potential damages that can result from deepfake attacks. To combat this, financial institutions can implement several measures. Routinely reviewing internal controls and updating processes can reflect the evolving risk environment. Utilizing multi-factor authentication can help verify the authenticity of transactions. Implementing dual authorization processes for individuals with account access, payments responsibilities, and other financial roles can also help prevent fraud.
Enabling receipt of fraud alerts when there is suspicious account activity can further aid in preventing fraud. Ongoing and mandatory fraud awareness training can help employees recognize common schemes and suspicious behaviors. Being asked to call a different number should always be a red flag.
Financial institutions can help customers be aware of common fraud schemes and understand how to help mitigate the risk. For instance, customers should be advised to verify transactions by contacting the sender or recipient using a known number, rather than relying on the one provided in the suspicious communication.
The development of GANs has been a significant advancement in deepfake technology. GANs use machine learning to create realistic copies of individuals based on audio and video recordings. While this technology has many potential applications, it also poses significant risks.
The finance and banking sector is particularly at risk from deepfake incidents due to the ability to create convincing replications of anyone's voice and image. As such, it is crucial for financial institutions to stay vigilant and proactive in their efforts to combat deepfake-related fraud.
The rise of deepfakes in the finance sector serves as a stark reminder of the importance of staying informed and adapting to new threats. Max Headroom, a fictional computer-generated TV character from the 1980s, is an early example of a character that resembles modern deepfakes. While the technology has come a long way since then, the principles of vigilance and adaptability remain the same.
References:
[1] Onfido's 2024 Identity Fraud Report. [2] Sumsub Report on Deepfake Incidents in the Fintech Sector (2023). [3] Deloitte's Center for Financial Services Predictions (2023). [4] Various academic publications on the use of Generative Adversarial Networks in deepfake creation and detection.
Artificial-intelligence-driven deepfakes, particularly those using Generative Adversarial Networks (GANs), are of significant concern to the business sector, especially finance. In the years 2022 to 2023, such deepfake incidents escalated in the finance sector, leading to financial losses and increased risks of identity theft, account takeover fraud, and reputational damage.
To counteract these threats, financial institutions should employ measures such as routinely reviewing internal controls, updating processes, utilizing multi-factor authentication, implementing dual authorization, enabling fraud alerts, providing ongoing fraud awareness training, and informing customers about common fraud schemes.
