Deepfake technology is a form of AI that uses deep learning algorithms to learn how to solve problems using large data in order to create convincing fake media. Most commonly, deepfake technology is used to swap faces into digital content for the purposes of fraud. Deepfake technologies make use of deep neural networks to employ face-swapping techniques that can map one person's image onto another person.

But how does this work?

Fraudsters take a collection of video clips of the target person and run them through an autoencoder. The autoencoder analyzes the video clips to generate an image of the target from a range of different angles. This image is then rendered onto another person, allowing them to gain access to the target's accounts. Deepfake technology is becoming more sophisticated and machine learning Generative Adversarial Networks, or GANs, are making it easier for fraudsters to bypass deepfake decoders by improving the flaws in their images.

As a result of improving technology, deepfake fraud is now a growing threat to the cybersecurity of fintech and digital goods companies. Deepfakes facilitate identity fraud through large-scale phishing and BEC attacks. Over time, this has the potential to do real damage to an organization's reputation and the security of employee data.

How Deepfake Technology Can Be Used for Financial Fraud

Deepfake technology is now being used to facilitate financial fraud in a number of different ways. Everything from identity theft to large-scale cyberattacks and the spreading of misinformation are propped up by deepfake technology. Let's take a look at some of the ways deepfake technology can be used for financial fraud.

Ghost Fraud

Ghost fraud is a type of scam in which a criminal steals the personal data of a deceased person for the purpose of financial fraud. These fraudsters steal a deceased person's identity to gain access to their online services and accounts or even take out credit cards and loans in their name.

New Account Fraud

New account fraud involves using a fake or stolen identity to open a new bank account. Using these fake or stolen identities, fraudsters open accounts to take out loans or max out credit cards without ever paying back the money.

Synthetic Identity Fraud

Synthetic identity fraud involves creating a fake identity based on the information and identities of multiple people. By pooling the information of multiple people, fraudsters make their activities harder to track down. Criminals then use this data to issue large transactions and credit applications.

How FinTechs Usually Deal With Deepfake Financial Fraud

In order to deal with deepfake financial fraud, FinTechs implement payment fraud protection strategies and anti-spoofing technologies. Biometrics is one of the most effective ways to deal with deepfake financial fraud. Biometric technologies help to prevent payment fraud in crypto and financial services by providing organizations with a secure way to authenticate online users using biometric face verification tools that allow users to verify their face against an official image (a passport or ID card, for example).

Another method used by FinTechs to deal with deepfakes is liveness detection, a technology that can identify artificial representations like deepfakes. This technology can be implemented when onboarding new clients to prevent fraudsters from setting up accounts under false identities or attempting fraudulent logins to existing user accounts.

Whether using biometric checks, liveness detection technology, or simple manual checks, there are some tell-tale signs that can help organizations detect deepfake financial fraud. Key indicators that an image or video may be a deepfake include:

• Variations in skin tone
• Variations in lighting
• Jerky movements
• Unnatural motions (e.g. blinking strangely or not at all)
• Out of sync speech-to-lip movements

A Better Way to Detect Deepfake Financial Fraud

Relying on manual checks alone is a pretty risky approach to dealing with deepfake scams. Luckily, anti-deepfake technologies are becoming more advanced. The best way to counter financial fraud is to implement a robust security procedure using a fraud prevention platform like nSure.ai, an advanced fraud detection and prevention platform designed to protect merchants of digital goods and high-risk digital domains by using tailored auto-ML models that collect and analyze real-time data, providing real-time anomaly detection, advanced behavioral analytics, and ancillary feedback loops. Schedule a demo today.