Ultimate FINGPT for Financial Analysis

Turn Financial Data into Decisions with the Power of FinGPT.

Key Features

● Hands-on setup of FINGPT in real-world finance projects.
● End-to-end guide for automating financial reporting tasks.
● Case studies on market trends and sentiment prediction.
● Techniques to scale, fine-tune, and optimize FINGPT models.

Book Description

FINGPT is redefining how financial institutions analyze data, forecast trends, and make strategic decisions. As the financial sector embraces generative AI, understanding and applying FINGPT becomes essential for professionals seeking to stay competitive and innovative.

Ultimate FINGPT for Financial Analysis takes you on a complete journey—from setting up your development environment and preparing financial datasets to building, fine-tuning, and deploying FINGPT models. The book covers all the vital concepts such as data cleaning, model training, prompt engineering, and real-world deployment. You will learn to automate financial reporting, generate accurate forecasts, perform sentiment analysis on news and reports, and simulate risk scenarios. Dedicated chapters on case studies and performance optimization provide deep insights into practical applications, while ethical considerations and scaling strategies ensure readiness for enterprise use.

Hence, whether you are a finance expert aiming to integrate AI or a data scientist expanding into fintech, this book provides the tools, frameworks, and confidence to apply FINGPT in your work. So, do not get left behind—start transforming your financial analysis with AI today.

What you will learn

● Apply FINGPT for financial forecasting and workflow automation.
● Build sentiment-aware models for trend and event prediction.
● Combine structured and unstructured data for deep insights.
● Generate reports and analytics, using AI-powered pipelines.
● Simulate risk scenarios, and plan proactive mitigations.
● Monitor FINGPT performance, using finance-specific KPIs.

Who is this book for?

This book is ideal for finance professionals, data scientists, and AI enthusiasts aiming to apply generative AI in financial analysis. A foundational understanding of Python, machine learning, and basic financial concepts will help readers fully benefit from the hands-on examples and case studies.

1. Introduction to FINGPT
2. Setting up the Development Environment
3. Cleaning and Preparing Financial Data
4. Fine-Tuning and Training a FINGPT Model
5. Case Studies in Financial Analysis
6. Automating Financial Reports with FINGPT
7. Market Trend Prediction with FINGPT
8. Sentiment Analysis in Finance with FINGPT
9. Model Performance Optimization and Scaling
10. Future Directions, Summary, and Conclusion
Index

Dr. Jignasha Shah Dalal is a leading voice in AI-enabled business transformation, blending over eighteen years of experience in technical education, academic leadership, and enterprise training. She bridges deep academic knowledge with real-world impact. Her expertise covers Generative AI, Agentic AI, Blockchain Security, AI-driven analytics, and privacy-preserving Machine Learning, with a strong focus on ethical and explainable AI. She has shaped technology education in India through curriculum innovation at Mumbai University, introducing industry-aligned modules in Blockchain, Data Structures, and Compiler Design. A sought-after speaker and corporate trainer, Dr. Dalal delivers high-impact workshops on Power BI, Microsoft Copilot, Secure AI, and AI for Finance, helping professionals across BFSI, technology, and education sectors harness AI for strategic advantage. In this book, she explores FINGPT, a generative AI tool for financial analysis, offering practical strategies and ethical guidance. Beyond work, she enjoys travel, reading, and mentoring. Dr. Dalal advocates for the intelligent, ethical, and purposeful use of technology.

About the Technical Reviewer

Dr. Santhilata Kuppili Venkata is a computer scientist, author, and entrepreneurial data-science leader whose work bridges advanced AI research and real-world applications. She earned her PhD in Computer Science from King’s College London and has applied AI in finance, insurance, cancer genomics, and archival studies. Passionate about finance, she founded an AI-backed financial services venture, developing FINGPT, a generative AI framework powered by Retrieval-Augmented Generation (RAG) over SEC filings. She led the creation of transformer-based models to extract regulatory disclosures, footnote tables, and risk factors from financial reports. Previously, she served as lead data scientist in insurance, deploying predictive models, and consulting for healthcare non-profits. As a Senior Research Fellow at the University of Birmingham, she developed an email context-extraction tool that earned multiple grants and facilitated collaborations with the University of Maryland and Stanford. Her postdoctoral work at the Francis Crick Institute advanced machine-learning methods for cancer prognosis. With over fifteen years of teaching in the UK and India, numerous publications, and mentorship roles, Dr. Santhilata blends scholarly rigor, engineering expertise, and entrepreneurial vision to drive AI innovation in finance.