Table of Contents
Question
You’re a director for an organization that detects credit card data. You’re trying to convince your manager to adopt a generative adversarial network (GAN) to test your system to see if it can identify credit card fraud.
What’s one of the best arguments you have for using this type of neural network?
A. This type of neural network arrangement will be the easiest for your organization to set up.
B. A GAN would allow the system to invent fraudulent transactions that aren’t present in the datA.
C. Fraudulent transactions are by their very nature adversarial, so it’s good to have a network that reflects this.
D. This type of system will generate many more fraudulent transactions than you would get with a typical neural network.
Answer
B. A GAN would allow the system to invent fraudulent transactions that aren’t present in the datA.
Explanation
The answer is B. A GAN would allow the system to invent fraudulent transactions that aren’t present in the data.
This is a strong argument for using a GAN to test a credit card fraud detection system because it allows the system to be tested against fraudulent transactions that it has never seen before. This is important because fraudulent transactions are constantly evolving, and a system that can only detect known fraudulent transactions is not going to be very effective.
A GAN works by pitting two neural networks against each other. One network, the generator, is responsible for creating new data that is similar to the data it was trained on. The other network, the discriminator, is responsible for determining whether the data is real or fake.
In the context of credit card fraud detection, the generator could be used to create new fraudulent transactions that are similar to the real fraudulent transactions in the data set. The discriminator could then be used to determine whether the new transactions are real or fake.
This process allows the system to learn how to identify fraudulent transactions that it has never seen before. This is a valuable capability because it helps to ensure that the system is not fooled by new fraudulent techniques.
The other answer choices are incorrect.
- A. This type of neural network arrangement will be the easiest for your organization to set up. This is not correct. GANs are a complex type of neural network, and they can be difficult to set up and train.
- C. Fraudulent transactions are by their very nature adversarial, so it’s good to have a network that reflects this. This is true, but it is not the best argument for using a GAN. The best argument is that GANs can be used to generate new fraudulent transactions that the system has never seen before.
- D. This type of system will generate many more fraudulent transactions than you would get with a typical neural network. This is not necessarily true. The number of fraudulent transactions that a GAN generates depends on the parameters of the GAN and the data set that it is trained on.
Reference
- Generative Adversarial Network-Based Credit Card Fraud Detection | SpringerLink
- [1907.03355] Improving Detection of Credit Card Fraudulent Transactions using Generative Adversarial Networks (arxiv.org)
- 1907.03355v1.pdf (arxiv.org)
- Credit Card Fraud Detection using GAN | Kaggle
- GAN-based Data Augmentation for Credit Card Fraud Detection | IEEE Conference Publication | IEEE Xplore
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