This AI-driven technology, once a tool for visual effects in movies, has now evolved into a major security threat in banking. Fraudsters are using deepfake to bypass biometric security systems, posing risks to financial institutions and customers.
The Rise of Deepfake Fraud in Banking
Deepfake is an artificial intelligence (AI) technology used to create highly realistic fake content, including videos, images, and voices. Initially popular in entertainment, it has now become a serious cybersecurity concern.
Cybercriminals use deepfake to manipulate biometric security systems, such as facial recognition, to impersonate individuals. In Indonesia, deepfake fraud cases have surged dramatically. Between 2022 and 2023, incidents of deepfake-related fraud increased tenfold, with a 1,550% surge across Asia-Pacific, including Indonesia. This data underscores how deepfake has become a new tool for sophisticated fraud.
Deepfake Enhances Social Engineering and Identity Takeover
AI-generated media is increasingly dangerous as it allows fraudsters to replicate identities or create entirely new ones. This capability makes deepfake a powerful tool for psychological manipulation, also known as social engineering.
Deepfake-generated videos, images, and voice recordings are being used to trick victims into trusting fraudulent transactions or requests.
According to VIDA, 72% of business professionals in Indonesia view security threats in the financial technology (fintech) sector as critical risks. They believe deepfake fraud not only causes financial losses but also severely damages business reputations.
Additionally, 31% of Indonesian business professionals consider money scamming to be the most common form of deepfake fraud. This scam involves criminals impersonating bank officials through deepfake voice or video calls, deceiving victims into transferring money to fraudulent accounts.
Five Types of Deepfake Fraud in Banking
Globally, cybercriminals are leveraging deepfake technology to exploit the banking industry using increasingly sophisticated methods. Based on industry research by VIDA, the five most significant types of deepfake fraud in banking include:
1. Money Scamming
This is the most common and widely known deepfake fraud. Criminals use deepfake video or voice technology to impersonate bank officials, corporate executives, or even family members, persuading victims to transfer funds to fraudulent accounts.
2. Identity Theft
Deepfake technology is used to bypass biometric security systems, such as facial recognition or voice authentication. Fraudsters create fake identities by combining stolen personal data with AI-generated faces or voices. These identities are then used to open bank accounts, apply for loans, or gain unauthorized access to financial platforms.
3. CEO Fraud
Also known as business email compromise, this attack involves deepfake-generated audio or video impersonations of corporate executives. Cybercriminals use these fake communications to trick employees into making unauthorized fund transfers or disclosing sensitive financial information.
4. Ghost Fraud
This fraud involves exploiting the personal data of deceased individuals. Using deepfake, fraudsters "bring back" the deceased to apply for loans, open bank accounts, or commit financial fraud.
5. New Account Fraud
Deepfake-generated identities are used to manipulate bank employees or customers through social engineering tactics. Fraudsters convince them to share sensitive data or approve unauthorized transactions.
How to Combat Deepfake Fraud in Banking
As deepfake technology evolves, the methods to detect and prevent digital fraud must also advance. Below are four key techniques banks should implement to counter deepfake threats:
1. Anti-Spoofing Algorithms
Banks must adopt technologies that detect and prevent presentation attacks. These attacks involve fraudsters using:
- 2D paper masks
- 3D masks
- Digitally altered images
- Deepfake videos
These techniques are designed to bypass facial recognition systems. Implementing anti-spoofing detection can significantly reduce the risk of fraudulent biometric authentication.
2. Biometric Verification with Silent Liveness Detection
Integrating silent liveness detection into biometric verification ensures that the verified individual is physically present and not an AI-generated video or manipulated image.
3. Real-Time Facial Matching with Official Databases
Banks should deploy real-time biometric processing and one-to-one matching against official data sources. This ensures that biometric data is authentic and matches a verified identity, providing strong protection against AI-driven fraud.
4. Device Verification
Linking user identity with device credentials adds an extra layer of security. For example, if a user logs in from a new device, the system should require authentication on the original device before granting access. This approach makes it harder for fraudsters to exploit synthetic content or deepfake media for unauthorized access.
VIDA’s Solution for Deepfake Fraud Prevention
To address the growing threat of digital fraud, VIDA Identity Stack offers a comprehensive digital identity protection solution. It is built on three key pillars:
1. Identity Proofing
VIDA’s verification process combines biometric authentication and liveness detection to ensure that only legitimate individuals can access financial systems.
2. User Authentication
VIDA employs multi-factor authentication (MFA) to block unauthorized access, even if login credentials are compromised.
3. Fraud Detection
VIDA’s fraud detection technology can identify suspicious activities, including deepfake manipulation during identity verification.
Originally developed for entertainment, deepfake has now become a major cybersecurity threat in banking. With a tenfold increase in deepfake fraud cases in Indonesia over the past year, it is critical for the banking industry to adopt advanced security measures.
Solutions like VIDA Identity Stack can help financial institutions safeguard their operations against AI-driven fraud, ensuring secure transactions and protecting customer identities.
.png)
