Poster: Exploring the Limits of Audio-Based Detection of Turkish Phone Call Scams

This research introduces the first public multi-modal dataset of 100 aligned audio-transcript pairs specifically for Turkish phone call scams, addressing the scarcity of annotated data in low-resource languages. The study evaluates seven large language models, including Gemini 2.5, GPT-4o, and Qwen, across different input conditions, finding that transcript-based inputs consistently outperform direct audio processing. This work underscores the necessity for culturally inclusive AI safety measures and more effective multi-modal systems in combating fraud in underrepresented languages.