# Advanced Real-Time Fraud Detection Using RAG-Based LLMs

arXiv: https://arxiv.org/abs/2501.15290 · HTML: https://mlsanity.com/paper/2501.15290/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 25 Jan 2025.

## Builds on (most cited first, 6 of 6)

- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024)
- [Detecting Scams Using Large Language Models](https://mlsanity.com/paper/2402.03147/) (2024)
- [An analysis of scam baiting calls: Identifying and extracting scam stages and scripts](https://mlsanity.com/paper/2307.01965/) (2023)
- [Fraud detection in telephone conversations for financial services using linguistic features](https://mlsanity.com/paper/1912.04748/) (2019)
- [Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs](https://mlsanity.com/paper/2406.03614/) (2024)

## Built on by (most cited first, 7 of 7)

- [TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection](https://mlsanity.com/paper/2503.24115/) (2025)
- [Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection](https://mlsanity.com/paper/2512.13040/) (2025)
- [Domain Knowledge-Enhanced LLMs for Fraud and Concept Drift Detection](https://mlsanity.com/paper/2506.21443/) (2025)
- [SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning](https://mlsanity.com/paper/2601.01392/) (2026)
- [AI-in-the-Loop: Privacy Preserving Real-Time Scam Detection and Conversational Scambaiting by Leveraging LLMs and Federated Learning](https://mlsanity.com/paper/2509.05362/) (2025)
- [Banking Done Right: Redefining Retail Banking with Language-Centric AI](https://mlsanity.com/paper/2510.07645/) (2025)
- [Reinforcement Learning of Large Language Models for Interpretable Credit Card Fraud Detection](https://mlsanity.com/paper/2601.05578/) (2026)

## Abstract (arXiv)

Artificial Intelligence has become a double edged sword in modern society being both a boon and a bane. While it empowers individuals it also enables malicious actors to perpetrate scams such as fraudulent phone calls and user impersonations. This growing threat necessitates a robust system to protect individuals In this paper we introduce a novel real time fraud detection mechanism using Retrieval Augmented Generation technology to address this challenge on two fronts. First our system incorporates a continuously updating policy checking feature that transcribes phone calls in real time and uses RAG based models to verify that the caller is not soliciting private information thus ensuring transparency and the authenticity of the conversation. Second we implement a real time user impersonation check with a two step verification process to confirm the callers identity ensuring accountability. A key innovation of our system is the ability to update policies without retraining the entire model enhancing its adaptability. We validated our RAG based approach using synthetic call recordings achieving an accuracy of 97.98 percent and an F1score of 97.44 percent with 100 calls outperforming state of the art methods. This robust and flexible fraud detection system is well suited for real world deployment.