Designing a Novel Voice-Driven System for Socio-economic Stress Detection in Fintech Professionals and Consumers

This study introduces a novel voice-driven stress detection system for the fintech industry, utilizing advanced machine learning techniques to address socio-economic stress. The proposed method initiate by preprocessing raw audio recordings to extract key features, such as Mel-Frequency Cepstral Coefficients (MFCCs), alongside vocal biomarkers including pitch, tone, and speech rate, which are essential for capturing the nuanced stress indicators prevalent in fintech’s high-pressure environments, such as volatile markets, tight deadlines, and intense client interactions incorporating Convolutional Neural Network (CNN). The proposed CNN model achieves 87 % accuracy, outperforming previous Speech Emotion Recognition benchmarks, and provides real-time support to enhance psychological resilience and productivity in high-pressure finance roles. Future research will incorporate additional vocal and behavioral data, expanding dataset diversity with fintech-specific recordings and integrating the system into comprehensive fintech platforms. These advancements aim to revolutionize mental health support in finance, offering a proactive, data-driven solution to mitigate stress-related risks, reduce burnout, and improve performance across the industry, particularly in emerging markets.