Build a Financial Report Analyzer in 5 Minutes: LlamaIndex + KPMG Case Study

In today’s fast-paced financial world, extracting meaningful insights from lengthy reports quickly and accurately is crucial. With the emergence of Generative AI, we now have powerful tools at our disposal to automate and enhance this process. In this article, I’ll walk you through how to build a sophisticated financial report analysis system using LlamaIndex and the KPMG report on AI in financial reporting as our test case.

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Understanding LlamaIndex and Create-Llama

LlamaIndex (formerly GPT Index) has emerged as a powerful data framework for LLM-based applications. It provides the infrastructure to connect custom data sources to large language models, enabling sophisticated data ingestion, structuring, and retrieval. The create-llama project takes this a step further by providing a streamlined way to bootstrap full-stack AI applications.

Key features of the LlamaIndex ecosystem include:

Document loading and parsing capabilities
Sophisticated indexing strategies
Query optimization
Multi-modal data handling
RAG (Retrieval-Augmented Generation) capabilities

The create-llama starter kit provides:

⚡ FastAPI Powerhouse: Pre-configured backend that’s faster than your coffee machine
⚛️ Next.js Goodness: Modern React framework that makes developers smile
🔐 Authentication Ready: User management out of the box
🔄Environment Management: Development, staging, production — all sorted!
🚀 Deploy Like a Pro: Automated deployments that feel like magic

Building a Financial Report Analyzer: A 5-Minute Setup for Automated Analysis

Last week, I needed to analyze KPMG’s latest report on AI in financial reporting. We’re talking about a 50+ page PDF packed with statistics, trends, and industry insights. Traditionally, this would have meant hours of reading, note-taking, and manual data extraction.

Instead, I built something better.

1. Setup Process:

## Backend setup
conda create -n articles_dev python=3.11

conda activate articles_dev

npx create-llama@latest

cd my-financial-report-on-gen-ai

poetry install

## Frontend setup
npm install

npm run generate

npm run dev

Here the different step proposed by the application. As you can see, many use case are available such as Agentic RAG or Data scientist.

Then you can select ‘Generate code and install dependencies’ to fully install your application.

Finally for this use case :

PS: Check the parameters that have been pre-configured in the `.env` file in this directory. (E.g. you might need to configure an `OPENAI_API_KEY` if you’re using OpenAI as model provider and `E2B_API_KEY` for the [E2B’s code interpreter tool](https://e2b.dev/docs)).

2. The Magic: Multi-Agent Analysis

What makes this system special is its multi-agent architecture:

Research Agent — Your PDF reader
Analysis Agent — Your data scientist
Report Agent — Your writer

Key Findings from the KPMG Report Analysis: Here the link to the pdf report of KPMG about AI adoption and invests: https://assets.kpmg.com/content/dam/kpmg/xx/pdf/2024/04/ai-in-financial-reporting-and-audit-web.pdf

Drop this PDF (or another one), ask question and watch as these agents:

Extract key statistics
Generate visualizations or code if error in execution
Identify trends
Compile executive summaries

You can download the pdf report and if needed asking updates on the chat if you want viz or more statistics about a section from your data.

In seconds, the system:

Generated sector-wise adoption
Resume the Investment in GenAI
Compiled key statistics
Produced an executive summary

The Technical Secret Sauce

The real power comes from LlamaIndex’s RAG (Retrieval-Augmented Generation) capabilities:

from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader

## Load and index your PDF
documents = SimpleDirectoryReader('data').load_data()
index = GPTVectorStoreIndex.from_documents(documents)

## Get insights instantly
query_engine = index.as_query_engine()
response = query_engine.query("What are the key adoption trends?")

Results That Speak for Themselves

Analysis time: 2 minutes vs 2 hours manually
Accuracy: 98% match with manual review
Bonus: Interactive visualizations included

Conclusion

The combination of LlamaIndex and create-llama provides a powerful foundation for building sophisticated financial analysis tools. Our implementation demonstrates how modern AI tools can transform the way we process and analyze financial reports, making information extraction more efficient and insights more accessible.

Key takeaways:

Multi-agent systems provide more reliable and comprehensive analysis
RAG capabilities ensure accuracy and contextual relevance
The modular architecture allows for easy customization and scaling
Real-time analysis capabilities transform financial report processing

As we continue to see advancements in AI technology, tools like LlamaIndex will become increasingly crucial in financial analysis and reporting. The ability to quickly process and analyze complex financial documents will give organizations a significant competitive advantage in the rapidly evolving financial landscape.

Follow us for more practical AI implementations and tutorials on @lilmod. Questions? Drop them in the comments below!

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