A Review of Financial Data Analysis Techniques for Unstructured Data in the Deep Learning Era: Methods, Challenges, and Applications

Jackson Duane, Ashley Morgan and Emily Carter

No gdvbj_v1, OSF Preprints from Center for Open Science

Abstract: Financial institutions are increasingly leveraging---such as text, audio, and images---to gain insights and competitive advantage. Deep learning (DL) has emerged as a powerful paradigm for analyzing these complex data types, transforming tasks like financial news analysis, earnings call interpretation, and document parsing. This paper provides a comprehensive academic review of deep learning techniques for unstructured financial data. We present a taxonomy of data types and DL methods, including natural language processing models, speech and audio processing frameworks, multimodal fusion approaches, and transformer-based architectures. We survey key applications ranging from sentiment analysis and market prediction to fraud detection, credit risk assessment, and beyond, highlighting recent advancements in each domain. Additionally, we discuss major challenges unique to financial settings, such as data scarcity and annotation cost, model interpretability and regulatory compliance, and the dynamic, non-stationary nature of financial data. We enumerate prominent datasets and benchmarks that have accelerated research, and identify research gaps and future directions. The review emphasizes the latest developments up to 2025, including the rise of large pre-trained models and multimodal learning, and outlines how these innovations are shaping the next generation of financial analytics.

Date: 2025-06-25
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:gdvbj_v1

DOI: 10.31219/osf.io/gdvbj_v1

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