AI-Powered Financial Analyst: Evaluating Company Performance for Investment Decisions

Abstract

Investing in businesses involves a deep understand- ing of their financial health, history, and future potential. Financial analysis is an essential component of investment decisions, which involves detailed studies that are often challenging for individuals without experience to carry out. The current research introduces an AI-based financial analysis platform that leverages the capabilities of Large Language Models (LLMs) to simplify the complex processes of data gathering, analysis, and documentation. The platform integrates with reliable financial data sources and analyzes up to four years of historical data for an actionable insight tailored to the particular type of industry. This study explores the methodology, advantages, and consequences associated with such a system, focusing on its capacity to democratize financial knowledge, minimize human error, and facilitate informed decision-making. By closing the divide between unprocessed data and actionable insights, the AI-driven solution streamlines the investment process for both professional experts and retail investors.

Introduction

1. Background

• Traditional financial analysis relies on large datasets like balance sheets and income statements.

• It requires expert knowledge and time, limiting accessibility for non-experts.

2. Problem Statement

• Complexity: Financial data is hard to interpret without specialized knowledge.

• Accessibility: Non-experts struggle to analyze financial indicators.

• Scalability: Existing tools can’t analyze real-time data across many companies simultaneously.

3. Proposed Solution

• An AI-powered financial analysis platform is developed using Large Language Models (LLMs).

• The system:

  • Fetches real-time financial data.

  • Analyzes historical trends and industry specifics.

  • Predicts future performance.

  • Automates report generation and supports investment decisions.

4. Types of Financial Analysis

• Descriptive: Summarizes past performance.

• Diagnostic: Identifies causes behind financial trends.

• Predictive: Forecasts future outcomes using AI models.

• Comparative: Benchmarks against industry or competitors.

• Prescriptive: Offers strategic recommendations (e.g., reduce debt, improve efficiency).

5. Case Study: Manufacturing Company

• Used financial data from sources like Yahoo Finance and SEC filings.

• AI agents analyzed metrics like revenue growth and debt-to-equity ratio.

• Findings: Strong revenue growth, but operational inefficiencies and high debt.

• Recommendations: Cost-cutting through tech and debt refinancing.

6. Key Benefits

• Accessibility: Enables individuals to make informed decisions without experts.

• Efficiency: Automates time-consuming analysis.

• Equity: Levels the playing field for retail investors.

7. Ethical & Economic Considerations

• Data Privacy: Ensuring secure handling of sensitive information.

• Bias in AI: Must avoid favoring specific companies or sectors.

• Economic Impact: Reduces monopoly on financial expertise, promoting market competition.

8. Challenges & Mitigation

• Data Issues: Handled through better cleaning and validation.

• Prediction Limits: Models updated regularly with diverse data.

• Security Risks: Use encryption, secure APIs, and legal compliance (e.g., GDPR, CCPA).

9. Future Research Directions

• Real-time data integration for live analysis.

• Advanced models like reinforcement learning.

• Sentiment analysis from social media/news.

• Multi-agent systems for tasks like ESG compliance.

• Cross-industry benchmarking for deeper insights.

Conclusion

• The AI-powered financial analysis platform simplifies in- vestment decision-making by automating complex tasks. • It provides solid, industry-specific knowledge and rec- ommendations, catering to both analysts and individual investors. • Future developments will focus on: – Real-time integration. – Advanced analytics. – Global applicability to maintain relevance in dynamic markets.

References

[1] Koller, T., Goedhart, M., &Wessels, D. Valuation: Mea- suring and Managing the Value of Companies. McKinsey & Company, 7th Edition. [2] Damodaran, A. The Little Book of Valuation: How to Value a Company, Pick a Stock and Profit. Wiley Finance. [3] Fabozzi, F. J. Financial Statement Analysis: A Practi- tioner’s Guide. Wiley. [4] Research Paper: ”Machine Learning Applications in Fi- nance: A Review of Literature and Future Directions” - Journal of Financial Data Science. WEB REFERENCES [1] Yahoo Finance - https://finance.yahoo.com: Used for gathering historical financial data. [2] OpenAI - https://openai.com: For leveraging GPT-based LLMs for advanced analytics. [3] LangChain - https://www.langchain.com: A framework for building LLM-powered applications. [4] Investopedia - https://www.investopedia.com: For under- standing financial metrics and investment strategies. [5] Fyres Brokerage - https://www.fyres.com: Financial API for historical data retrieval. OTHER REFERENCES [1] Tools and Platforms Used: – Python libraries: Pandas, NumPy, Matplotlib for data analysis and visualization. – LangChain for integrating the LLM into workflows. – GPT-4 API for natural language processing and financial insight generation. [2] Articles and Blog Posts: – ”How AI is Transforming Financial Analysis” – Blog on Medium. – ”The Role of Machine Learning in Investment De- cisions” – Article on Towards Data Science. [3] Datasets and APIs: – Historical stock price data: BSE, NSE, Yahoo Fi- nance API. – SEC EDGAR for company filings and financial state- ments.

Copyright

Copyright © 2025 Ayush Tripathi, Pratham Satpute, Dr. Pooja Raundale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.