Vol. 13 No. 3 (2025): Business & Management Studies: An International Journal
Articles

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

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  • Büşra Özdenizci Köse
Büşra Özdenizci Köse
Assoc. Prof., Department of Management Information Systems, Gebze Technical University, Kocaeli, Türkiye
Bio

Published 2025-09-25

Keywords

  • FinTech, Financial Technology, Financial Intelligence, Natural Language Processing, Sentiment Analysis, Strategic Decision-Making
  • FinTech, Finansal Teknoloji, Finansal Zekâ, Doğal Dil İşleme, Duygu Analizi, Stratejik Karar Verme

How to Cite

Sentiment analysis in FinTech: Trends and approaches in market, risk, and compliance. (2025). Business & Management Studies: An International Journal, 13(3), 1279-1292. https://doi.org/10.15295/bmij.v13i3.2633

Copyright (c) 2025 Büşra Özdenizci Köse

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Sentiment analysis in FinTech: Trends and approaches in market, risk, and compliance. (2025). Business & Management Studies: An International Journal, 13(3), 1279-1292. https://doi.org/10.15295/bmij.v13i3.2633

Abstract

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.

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