How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment

A novel neuro-symbolic AI approach has been developed that enables neural networks to autonomously discover interpretable rules, rather than relying on human-crafted rules. By integrating a differentiable rule-learning module into a hybrid neural network, the system was able to extract IF-THEN fraud detection rules during training on the Kaggle Credit Card Fraud dataset, which has a 0.17% fraud rate. This advancement demonstrates the potential for neural networks to enhance transparency and interpretability in complex tasks like fraud detection by autonomously deriving logical rules, thereby reducing reliance on manual rule specification. The learned rules, such as