LLM40FD: Unlocking the Potential of LLM for Anonymous Zero-Shot Fraud Detection

Credit card (CC) fraud detection within the realm of financial security faces challenges such as data imbalance, large-scale anonymized transaction datasets, and the need for system-specific model training. Past methods often fail to address these aforementioned issues simultaneously. Utilizing a single model results in a lack of zero-shot capability without adaptation for real-world scenarios. This article introduces LLM40FD, a novel framework that leverages a large language model (LLM) to overcome these obstacles in anonymous zero-shot fraud detection. LLM40FD addresses the aforementioned challenges in CC fraud detection by employing a distribution-based one-class function and the walking embedding, without reliance on labeled data or fine-tuning in downstream. Additionally, LLM40FD enhances the model’s ability to detect fraudulent patterns and define robust decision boundaries. This is achieved through a dual-augmentation strategy and implicit contrastive learning, which generate enriched positive and negative samples. Our experiments demonstrate that LLM40FD not only achieves state-of-the-art (SOTA) performance in the full-shot setting but also exhibits strong zero-shot capability even with limited training data. Furthermore, we conduct additional experiments to validate the effectiveness and working mechanism of LLM40FD.