Deepfakes proliferating: Understanding the surge of deceptive videos and safeguarding your corporate interests
In the rapidly evolving digital landscape, financial institutions are facing a new challenge: deepfake threats. These sophisticated forgeries, created using advanced machine learning techniques, are increasingly being used to perpetrate fraud in the finance and banking sector.
According to Onfido's 2024 Identity Fraud Report, there was a staggering 3000% increase in deepfake attempts from 2022 to 2023. This alarming trend has prompted financial institutions to adopt new strategies for deepfake detection and prevention.
The Role of Generative Adversarial Networks (GANs)
GANs, a type of machine learning, play a dual role in this battle. On one hand, they are used to create highly realistic deepfake content that fraudsters exploit. On the other hand, they serve as foundational technology for some detection tools that learn to spot synthetic artefacts by training on GAN-generated fakes.
However, the ongoing arms race between generative models and detection algorithms means that generators are currently advancing faster, producing increasingly nuanced fakes exhibiting micro-expressions, blinking, and voice emotion that challenge detectors.
Strategies for Deepfake Detection and Prevention
Financial institutions are adopting several strategies to combat deepfake-enabled fraud. These include:
- Multimodal forensic detection systems that analyze inconsistencies across audio, video, and image data to identify deepfake clues difficult for humans to detect.
- Voice biometric security integrated with behavioral analytics, though sophisticated voice cloning sometimes defeats these measures, prompting continuous updates.
- Agentic AI systems that combine real-time detection, authentication, and automated fraud response at scale in financial institutions.
- Collaborative efforts between banks, fintechs, and regulators to share intelligence and enhance adaptive authentication and fraud prevention frameworks.
- Exploring decentralized identity systems to improve verification security by reducing reliance on easily spoofed personal identifiers in KYC processes.
The Impact of Deepfake Attacks
Deepfake attacks can lead to significant risks for the finance and banking sector, including identity theft, account takeover fraud, and reputational damage. Notable cases of multimillion-dollar scams involving deepfake impersonations of executives to authorize fraudulent transfers highlight the severity of these threats.
Rapid improvements in AI-generated audio and video have rendered traditional cybersecurity tools inadequate, as these attacks do not involve malware but exploit social engineering amplified by deepfakes. Consequently, banks are moving beyond classic security to adaptive AI-driven analytics that monitor systemic fraud patterns across customer interactions and transaction flows.
Preventative Measures for Customers
While financial institutions are strengthening their defences, customers also play a crucial role in preventing deepfake attacks. Maintaining a close relationship with their banker can help customers catch fraud early. Additionally, customers should be vigilant and regularly review their accounts for any unusual activity.
Simple measures like verifying every customer and vendor transaction request with a phone call to the number on record can also help prevent deepfake attacks. Financial institutions like Synovus Bank offer assistance in mitigating Business Email Compromise (BEC) fraud, and customers can contact them for help.
The Future of Deepfakes in Finance
The technology for creating deepfakes is now readily available, requiring no experience. Deloitte's Center for Financial Services predicts that generative AI could enable fraud losses to reach $40 billion in the U.S. by 2027, up from $12.3 billion in 2023.
In this dynamic battle, financial institutions are relying heavily on continuously evolving AI detection tools and operational enhancements. However, no current detection approach is foolproof, and the sector faces ongoing challenges due to the rapid sophistication of generative AI creating highly realistic deepfake content.
In conclusion, financial institutions are engaged in a technology-driven battle against deepfake-enabled fraud, requiring constant vigilance and innovation to stay ahead of the curve. Customers and financial institutions alike must remain alert and proactive to protect against these increasingly sophisticated threats.
[1] FinVolution, "Deepfake Detection: How to Spot Deepfake Face Swap Attacks Aimed at Bypassing KYC Processes," [accessed 2023-04-10]. [2] Deloitte, "Generative AI: The Future of Fraud," [accessed 2023-04-10]. [3] Sumsub, "Deepfake Fraud in Fintech: A 700% Increase in 2022," [accessed 2023-04-10]. [4] Synovus Bank, "BEC Fraud Prevention," [accessed 2023-04-10].
- The advancements in artificial intelligence, including generative adversarial networks (GANs), have significantly impacted the finance and business sector, serving as both a tool for creating deepfake content exploited by fraudsters and as a basis for detection tools that learn to spot synthetic artefacts.
- Leveraging the power of technology, financial institutions are implementing innovative strategies such as multimodal forensic detection systems, voice biometric security, agentic AI systems, collaborative efforts, and decentralized identity systems to combat deepfake-enabled fraud and protect against identity theft, account takeover fraud, and reputational damage.
