# Combating Phone Scams with LLM-based Detection: Where Do We Stand?

arXiv: https://arxiv.org/abs/2409.11643 · HTML: https://mlsanity.com/paper/2409.11643/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 3 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Built on by (most cited first, 10 of 10)

- [TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection](https://mlsanity.com/paper/2503.24115/) (2025)
- [Open-Jev Judgments on CallScreenBench: Calibrated One-Pass Scam Screening with a Small Language Model](https://mlsanity.com/paper/2609.23959/) (2026)
- [Exposing LLM Vulnerabilities: Adversarial Scam Detection and Performance](https://mlsanity.com/paper/2412.00621/) (2024)
- [FraudShield: Knowledge Graph Empowered Defense for LLMs against Fraud Attacks](https://mlsanity.com/paper/2601.22485/) (2026)
- [Distinguishing Scams and Fraud with Ensemble Learning](https://mlsanity.com/paper/2412.08680/) (2024)
- [Anatomy of a Scam Call: What 10,000 real scam and spam calls reveal about how phone scammers operate](https://mlsanity.com/paper/2608.24127/) (2026)
- [One Size Fits All? A Modular Adaptive Sanitization Kit (MASK) for Customizable Privacy-Preserving Phone Scam Detection](https://mlsanity.com/paper/2510.18493/) (2025)
- [Scam Shield: Multi-Model Voting and Fine-Tuned LLMs Against Adversarial Attacks](https://mlsanity.com/paper/2511.01746/) (2025)
- [DG^VoiC: Speaker Clustering for Fraud Investigation under Real Call-Centre Conditions](https://mlsanity.com/paper/2606.28048/) (2026)
- [CallScreenBench: Benchmarking Small Language Models as Phone Secretaries](https://mlsanity.com/paper/2608.01033/) (2026)

## Abstract (arXiv)

Phone scams pose a significant threat to individuals and communities, causing substantial financial losses and emotional distress. Despite ongoing efforts to combat these scams, scammers continue to adapt and refine their tactics, making it imperative to explore innovative countermeasures. This research explores the potential of large language models (LLMs) to provide detection of fraudulent phone calls. By analyzing the conversational dynamics between scammers and victims, LLM-based detectors can identify potential scams as they occur, offering immediate protection to users. While such approaches demonstrate promising results, we also acknowledge the challenges of biased datasets, relatively low recall, and hallucinations that must be addressed for further advancement in this field