SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning
Abstract
Existing fraud detection methods predominantly rely on transcribed text, suffering from ASR errors and missing crucial acoustic cues like vocal tone and environmental context. This limits their effectiveness against complex deceptive strategies. To address these challenges, we first propose \textbf{SAFE-QAQ}, an end-to-end comprehensive framework for audio-based slow-thinking fraud detection. First, the SAFE-QAQ framework eliminates the impact of transcription errors on detection performance. Secondly, we propose rule-based slow-thinking reward mechanisms that systematically guide the system to identify fraud-indicative patterns by accurately capturing fine-grained audio details, through hierarchical reasoning processes. Besides, our framework introduces a dynamic risk assessment framework during live calls, enabling early detection and prevention of fraud. Experiments on the TeleAntiFraud-Bench demonstrate that SAFE-QAQ achieves dramatic improvements over existing methods in multiple key dimensions, including accuracy, inference efficiency, and real-time processing capabilities. Currently deployed and analyzing over 70,000 calls daily, SAFE-QAQ effectively automates complex fraud detection, reducing human workload and financial losses. Code: https://anonymous.4open.science/r/SAFE-QAQ.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the development of the SAFE-QAQ framework, which significantly enhances audio-based fraud detection by integrating reinforcement learning with slow-thinking processes, thereby addressing the limitations of traditional ASR-based methods. The technical contributions are substantial, with a well-defined methodology and rigorous experimental validation that positions this work as a notable advancement in the field of machine learning for fraud detection.
Methodology
The methodology is robust, introducing the SAFE-QAQ framework that integrates reinforcement learning with slow-thinking mechanisms to enhance audio-text fraud detection. The use of rule-based rewards and dynamic risk assessment during live calls is innovative, allowing the model to capture nuanced audio features that traditional ASR-based systems miss. The hierarchical reasoning process is well-structured, and the transition from raw audio processing to real-time fraud detection is a significant advancement in the field.
Results & evaluation
The experiments are comprehensive, utilizing the TeleAntiFraud-Bench dataset to demonstrate the effectiveness of the SAFE-QAQ framework. The reported improvements in accuracy, efficiency, and real-time processing capabilities are substantial, with detailed comparisons against various baseline models. The results indicate a clear performance hierarchy, showcasing the advantages of the proposed approach over existing methods.
Reproducibility
The paper provides detailed implementation details, including hyperparameter settings and the computational environment used for experiments. However, the lack of a publicly accessible dataset for broader testing may hinder full reproducibility. The code repository link is provided, which aids in replicating the results.
Limitations
One notable limitation is the reliance on the TeleAntiFraud-28k dataset, which may not encompass the full diversity of real-world fraud scenarios. This could restrict the generalization of the model's effectiveness across varied acoustic conditions. Additionally, the paper acknowledges potential algorithmic biases and emphasizes the need for continuous monitoring in practical applications.
Broader impact
The SAFE-QAQ framework has significant implications for the telecom industry, offering a more effective tool for fraud detection that can reduce financial losses and human workload. Its deployment in real-time systems could enhance the security of telecommunications, making it a valuable contribution to both academia and industry. The main contribution of this paper is the development of the SAFE-QAQ framework, which significantly enhances audio-based fraud detection by integrating reinforcement learning with slow-thinking processes, thereby addressing the limitations of traditional ASR-based methods. The technical contributions are substantial, with a well-defined methodology and rigorous experimental validation that positions this work as a notable advancement in the field of machine learning for fraud detection.
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