Finance, Generative AI

Book Review: The Future of Finance with ChatGPT and Power BI

My thoughts on the book written by James Bryant and Aloke Mukherjee, published by Packt Ltd.

Image by Author

Lately, I have seen various guides, books, courses, and more or less detailed articles circulating on the web on how to use Generative Artificial Intelligence and, in particular, ChatGPT to carry out various tasks. I am also working on integrating GenAI into data storytelling. However, I never thought that GenAI could be applied to the finance sector, given that it is a very delicate area requiring a lot of attention.

Regarding this thought, I had to change my mind when James Bryant contacted me asking for feedback on his book, The Future of Finance with ChatGPT and Power BI, given that we both deal with Generative AI applied to something (Finance in his case, Data Storytelling in my case).

The approach that James and Aloke take in their book is very interesting because, in addition to giving real examples of how to combine GenAI and Finance, they offer many valid theoretical principles regardless of the finance context. It is a book rich in content, proposed progressively so that the reader can gradually enter the topic.

In addition to the examples complete with Python code and related visualization in Power BI, the following things struck me:

• The authors provide pre-packaged prompts to ask questions relating to the financial sector. Use these prompts on your dataset and extract insights. These prompts are done so well that they can be generalized to other industries as well. My idea is to try using them on environmental data…but that’s another story. An example of a prompt? Here it is about how to extract revenue increase and its drivers:

input_text = f"{input_text}What is the percentage increase in revenue compared to the previous quarter, and what are the main drivers of this increase?"

• Personally, I am very attached to the topic of data storytelling and data visualization. The authors also give guidelines in this and, in particular, talk about data narratives. For example, they provide tips on how to build impactful visualizations, including understanding the audience, keeping visualizations simple, giving context, etc. All concepts that are very dear to me! Thanks, James at Aloke, for summarizing these principles :)
• The authors also describe how to use ChatGPT in different ways. I have taken a screenshot directly from the book to give an idea of what they suggest:

Image extracted from Chapter 3 of the book

• The authors also give guidelines with Python code on creating customized AI agents based on your needs.
• The authors also comment on important AI-related topics, such as bias, hallucinations, open-source vs. closed models, AI regulation, or AI literacy.
• The authors describe many concrete investment examples, such as Tesla and Salesforce, illustrating successful business strategies and market trends.

In addition to what struck me most and which I have just described, the book contains many ideas for learning and deepening financial analysis.

Book Table of Contents

Below I insert a brief index of the book just to give you an idea:

Part 1: From Financial Fundamentals to Frontier Tech: Navigating the New Paradigms of Data, EVs, and AgTech

• Chapter 1: Financial Mastery with ChatGPT: From Basics to AI Insights
• Chapter 2: Creating Financial Narratives with Power BI and ChatGPT
• Chapter 3: Tesla’s Financial Journey: AI Analysis and Bias Unveiled
• Chapter 4: John Deere’s AgTech Revolution — AI Insights and Challenges

Part 2: Pioneers and Protectors: AI Transformations in Software, Finance, Biotech, and Cybersecurity

• Chapter 5: Salesforce Reimagined: Navigating Software and LLMs
• Chapter 6: SVB’s Downfall and Ethical AI: Smart AI Regulation
• Chapter 7: Moderna and OpenAI — Biotech and AGI Breakthroughs
• Chapter 8: CrowdStrike: Cybersecurity in the Era of Deepfakes.

The Book in Summary

Below, I summarize some features of the book:

• Authors: James Bryant and Aloke Mukherjee
• Title: The Future of Finance with ChatGPT and Power BI
• Publisher: Packt Lt.
• Number of Chapters: 8
• Who can read this book: python developers, financial data analysts, or aspiring financial data analysts.

Do you find the book exciting and want to know more? Visit its official page!

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Book Review: Effective Data Analysis by Mona Khalil

Data Storytelling with Generative AI