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@article{183190,
        author = {Jennifer Nancy and Chaitra S and Chaithrakala R},
        title = {Natural Language Processing in Financial Reporting: Challenges and Opportunities},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {433-441},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183190},
        abstract = {The integration of Natural Language Processing (NLP) in financial reporting represents a significant advancement in data analytics and automation. This research explores the transformative potential of NLP in enhancing the efficiency, accuracy, and transparency of financial disclosures. By leveraging machine learning and advanced linguistic models, NLP enables the extraction and interpretation of vast unstructured financial data, supporting real-time reporting and strategic decision-making. However, challenges such as domain-specific jargon, model interpretability, data quality, and compliance with regulatory standards must be addressed. The study emphasizes the need for collaboration between data scientists and financial experts to develop reliable and context-aware NLP applications. It also highlights the growing relevance of sentiment analysis, risk detection, and multilingual processing in modern financial environments. Comparative insights from both international and Indian contexts reveal diverse challenges and opportunities in NLP adoption. Ethical considerations, bias mitigation, and stakeholder education are crucial to building trust in automated systems. Ultimately, NLP holds the potential to revolutionize financial reporting by automating routine tasks, enhancing forecasting capabilities, and making financial information more accessible to a broader audience.},
        keywords = {Natural Language Processing (NLP), Financial Reporting, Machine Learning, Sentiment Analysis, Regulatory Compliance, Data Automation, Predictive Analytics, Financial Transparency},
        month = {July},
        }