ScamDetector: Leveraging Fine-Tuned Language Models for Improved Fraudulent Call Detection

This paper introduces a fine-tuned Large Language Models (LLMs) for detecting scam calls, a response to the growing severity of telecommunication fraud in Singapore, which has seen a consistent increase in scam incidents over recent years. Our approach significantly enhances scam detection capabilities through the strategic augmentation of existing datasets with generative AI. This process involves consolidating multiple datasets and employing generative models to synthesize culturally relevant scam scenarios specific to the Singapore context. We use the augmented dataset to fine tune pretrained LLMs, including GPT-2 and Llama. We conducted extensive experiments to compare the effectiveness of traditional machine learning (ML) models, deep learning (DL) techniques, transformers, and our fine-tuned LLMs. The results clearly show that our fine-tuned LLM outperforms other models in terms of inference accuracy while maintaining acceptable inference times, thereby establishing itself as a practical tool for real-time scam detection. The source code of this work is made publicly accessible at https://github.com/ict-at-sit/ScamDetector.