Sentiment analysis in FinTech: Trends and approaches in market, risk, and compliance

As the financial technology (FinTech) sector increasingly embraces data-driven strategies, sentiment analysis has emerged as a critical tool for extracting actionable insights from unstructured textual data. This study explores the strategic applications of sentiment analysis in FinTech, with a particular focus on its roles in market prediction, risk assessment, and regulatory compliance. Drawing on a systematic thematic analysis of the academic literature, this study synthesises how financial institutions utilise sentiment analysis to enhance decision-making, mitigate risks, and address evolving customer behaviours and regulatory demands. The findings indicate that sentiment analysis enables early detection of market trends, supports more nuanced credit evaluations, and strengthens compliance monitoring by uncovering behavioural patterns and emotional signals across multiple data sources. The study also discusses practical challenges—including data quality, integration issues, and model bias—that must be addressed to realise its full potential. This work contributes to the literature by providing a structured thematic framework and identifying underexplored application areas of sentiment analysis in the FinTech sector. Future research may extend this study by incorporating empirical testing of domain-specific sentiment models.