A vast number of insights remain locked away in financial textual data—from contracts and market reports to customer feedback. Natural language processing (NLP), a branch of AI that transforms unstructured data into actionable intelligence, realizes much of that potential without overextending finance teams.
However, the complexity of human language presents continual challenges. As NLP methods progress via continuous improvement, interpreting textual data becomes increasingly achievable. However, limitations relative to human understanding persist. Businesses must creatively apply the latest capabilities of NLP in finance, understanding its limitations alongside its opportunities.
How NLP Understands Language
Essentially, NLP bridges the gap between human communication and data processing by teaching machines to comprehend language. It combines linguistics and computer science, empowering AI systems to analyze text, process speech, translate across languages and grasp nuanced meanings.
At its core, NLP ingests unstructured textual data and lends it some structure so algorithms can extract intelligence. This structuring has enabled innovations like voice assistants (e.g., Siri), spam filters and translation tools (e.g., Google Translate).
Turning Qualitative Data Quantitative
What sets NLP apart is its ability to decode qualitative data like financial news, earnings calls, financial reports and social media posts. Manually analyzing such data is immensely time-intensive, but NLP tools can rapidly process volumes of text to uncover market trends, inform investments, gauge customer sentiments and highlight risks—converting language into quantifiable, actionable insights.
The Advent of Sophisticated Large Language Models
Large language models (LLMs) like GPT-4 have seen rapid advancement, with some LLMs now demonstrating an unprecedented depth of comprehension and ability to interpret nuances in human language. These models ingest huge datasets, learning the intricacies of language and financial terminology to assist in strategic analysis.
NLP in Finance: Core Use Cases for Strategic Decision Making
According to a recent Paro survey, 40% of business executives working in finance and accounting currently use NLP for customer service, with 60% of smaller businesses under $10 million in revenue adopting the technology for this use case. However, much like machine learning models, NLP, now augmented with LLMs, can also help businesses with strategic planning and analysis.
Finance professionals can leverage NLP in three ways to augment their analytical capabilities:
- Sentiment analysis: Enhanced by LLMs, NLP helps inform decision makers on customer and market attitudes. NLP tools interpret signals from financial news, social media and more, offering a dynamic and in-depth perspective on market trends and sentiments.
- Financial reporting analysis: NLP tools can swiftly digest financial statements and reports to highlight key performance metrics and trends. By rapidly processing past and present data, finance professionals can analyze a company’s financial health with greater speed and sophistication.
- Risk management: By analyzing textual information, NLP assists in ensuring compliance and reducing risk exposure. NLP tools can, for example, scan lending agreements to assess obligations or interpret transactions and communication to detect anomalous or unethical activity.
Overcoming Limitations Through Continuous Learning
Despite major advances, NLP still grapples with grasping nuanced human language and adapting to evolving financial environments. The future of NLP in finance lies in continuous learning, where NLP systems repeatedly ingest new, diverse data to become more intuitive and attuned to change.
As NLP and AI capabilities grow in tandem, the future looks bright. We move closer to NLP tools that offer real-time, tailored insights to democratize financial analytics for all users.
Want to extract greater insights from your data? Paro’s network of fractional finance experts go beyond surface-level analytics, combining industry expertise with the latest advancements to help businesses extract maximum value from their data.
Whether enhancing legacy systems or building future-focused infrastructure, we become an integrated part of your team to elevate decision making. Schedule a free consultation to learn more.
About the Author
Kody Myers, Senior Director of Product at Paro, brings a decade of product management experience fueled by a passion for AI-driven solutions. Kody thrives in the ambiguous environment of early-stage, high-growth startups, developing long-term product and data strategies. His entrepreneurial and financial background, coupled with the analytical rigor developed during his time in market research, positions him at the forefront of AI product innovation in finance.
