Generative AI Fraud

A Generative Adversarial Network, or GAN, is a deep learning mechanism that’s used in generative AI. It has two neural networks and trains them to compete against each other in order to generate more accurate new data from the original training dataset. An example of a GAN would be to generate new images from an already existing image base. 

It’s called “adversial” because of its two networks:

• One network generates new data from an input sample (also known as the “generator”). 
• The other predicts whether this generated data fits the original dataset (in other words, it assesses the newly generated data to see if it’s genuine).

Some examples of generative AI fraud include bad actors creating the following content:

• Fake documents. Criminals use generative AI fraud and produce high-quality fake IDs at scale that are designed to bypass verification systems. Less sophisticated bad actors buy packages for “opening new bank accounts” on the dark web. This includes fake IDs, deepfake selfies, or face-swapping tools. 
• Deepfake videos. These AI-generated videos are used in fraudulent practices to pass liveness checks, which are used by regulated industries to ensure that the person completing selfie verification is the same person on their presented ID document during the onboarding process.
• Voice imitation. Through generative AI fraud, criminals can create deepfake voices that imitate real people and create loopholes to bypass voice verification systems, then commit account takeovers and other fraudulent activities.

To combat AI-generated fraud, many financial institutions and other high-risk businesses use  AI-powered fraud prevention solutions due to their ability to process large amounts of data in real-time, send alerts, and identify suspicious patterns. 

If you’re already using some sort of fraud detection solution and ID verification software, don’t focus primarily on detecting AI-generated fake IDs. Cross-check collected data with issuing databases and add extra features like NFC-enabled IDs or use behavioral and passive signals, such as device fingerprints, phone risk reports, general customer risk assessment tools, or extra steps like address verification.

In the legal industry, generative AI is still an emerging concept, which makes it challenging for companies to meet legal and ethical standards, especially when they have unclear guidelines. That’s because there is no fixed universal guide on the use of AI due to rapid technological changes. At the same time, critics argue that when litigators use generative AI to answer questions or draft legal documents based on specific use cases, they can share confidential information with third parties.

Yes, ChatGPT is a form of generative AI. However, AI is a broad field, and as a result, ChatGPT is only one specific example or tool of Generative AI. That’s because generative AI uses deep learning, which is based on neural networks designed to find patterns in large data sets. Using this principle, large language models (LLMs), in this case, ChatGPT, use transformer architectures to analyze and produce text from extensive training data.