README.md

metadata

license: cc-by-nc-4.0
language:
  - en
tags:
  - finance
  - economics
  - investment
  - corporate law
  - risk assessment
  - financial reporting
  - sustainability
  - llm-finetuning

📊 Finance & Decision-Making Conversations (Single-Turn JSONL)

📝 Dataset Summary

This dataset is a processed version of the [Comprehensive Finance & Decision-Making Conversations Dataset].
The original dataset contained multi-turn dialogues, but in this version each user–assistant exchange is stored as an independent data row in JSONL format.

It is designed for LLM fine-tuning where single-turn training examples are more practical and efficient.
Each conversation retains a domain-specific system instruction but focuses on a single user query and assistant response.

---

✨ Key Domains Covered

• Financial Decision Making
• Risk Assessment & Investment Strategies
• Cost-Benefit Analysis, Budgeting & Forecasting
• Financial Report Analysis
  • Balance Sheets
  • Income Statements
  • Cash Flow Statements
  • Ratio Analysis (ROE, ROA, Liquidity, Solvency, Debt-to-Equity)
• Detecting Trends & Anomalies
• Sector Comparison
• Finance Legal & Regulatory Frameworks
  • Corporate Law in Finance
  • Tax Regulations & Planning
  • Securities Law (Insider Trading, Market Manipulation)
  • Compliance & Anti-Money Laundering (AML)
• Mergers & Acquisitions (M&A)
• IPO & Capital Raising Strategies
• Financial Crisis Case Studies
  • 2008 Global Financial Crisis
  • Asian Financial Crisis
  • Other major downturns
• Global Macro-Economic Impact Analysis
• Sustainable Finance & ESG Investing

---

📂 Dataset Structure

Each entry is a single user–assistant pair, wrapped with a system instruction.
Format:

{
  "messages": [
    {"role": "system", "content": "You are a financial strategist providing in-depth analysis and structured guidance."},
    {"role": "user", "content": "What are the red flags in a company’s financial statement that may indicate insolvency?"},
    {"role": "assistant", "content": "Red flags include declining cash flow, negative working capital, high debt-to-equity ratios, and frequent restructuring charges."}
  ]
}