ChatGPTIn whatever capacity you might be working, AI has become a practical part of the workflow rather than a novelty. In finance, large language models are increasingly used to summarize, structure, and interpret complex data, helping teams turn raw information into clearer insights and forecasts. This pattern plays out daily across trading, research, and risk functions, particularly in equities and foreign exchange, where speed and clarity of information matter as much as the data itself.
AI-powered decision-making uses AI to help in financial analysis. Everyone has access to ChatGPT, but not everyone knows how to use its capabilities to analyze and make sense of all the numerical data. Structuring data for financial analysis is a key skill that many businesses, whether financial companies or grocery stores, require to understand their money situations.
In this tutorial, we’ll guide you on how to structure data for financial analysis, turning raw inputs into clean, usable datasets that make modeling, forecasting, and decision-making far more efficient.
By the end of this glorious tutorial, you’ll be able to:
Plan data structures for analysis
Organize data as unpivoted data
Categorize financial data
Create data relationships
Ensure data accuracy
Let’s dive right into it!
