How can artificial intelligence defeat credit card fraudsters?

Credit card fraud is becoming increasingly sophisticated, but artificial intelligence is developing new weapons to combat it. The main problem? Fraudulent transaction data is very rare compared to legitimate ones, making it difficult for machine learning models to learn to recognize them. To solve this problem, researchers have tested generative models such as Variational Autoencoders (VAEs), Autoencoders (AEs), Generative Adversarial Networks (GANs), and a hybrid AE-GAN. These models create synthetic fraud data, balancing the dataset and improving the models' ability to detect fraud. The results? Generative models outperform traditional oversampling methods like SMOTE and ADASYN, offering a more effective solution to protect our money.

Generative models like VAEs, AEs, GANs, and AE-GAN work by creating new data that mimics real data. For example, a VAE compresses data into a latent representation and then reconstructs it, generating new variants. GANs, on the other hand, use two neural networks: one generates synthetic data and the other evaluates it, improving the quality of the generated data. The hybrid AE-GAN combines the compression capabilities of Autoencoders with the adversarial generation of GANs. These models were tested on a real-world credit card transaction dataset, demonstrating greater effectiveness in detecting fraud compared to traditional methods. The BEFS (Balanced Error-Free Score) metric was used to evaluate performance, showing that generative models achieve higher scores, especially in class imbalance situations.