Tag
#data-quality
10 articles
- AI & Finance
Building an AI That Understands Financial Statements
Understanding a financial statement is not reading its words. It means normalising the data, respecting the accounting identities, and cross-checking every number against the other statements.
- AI & Finance
Structuring Decades of Filings So an AI Can Actually Use Them
A language model cannot reason over a messy pile of filings. Labels drift, statements get restated, formats change, and history is not what it looks like today.
- AI & Finance
The Engineering Challenges Behind Institutional AI
Institutional-grade financial AI is hard for five reasons: data quality, point-in-time correctness, citations, deterministic outputs, and coverage at scale. Here is each one.
- AI & Finance
Why Point-in-Time Data Matters in Research and Backtests
Point-in-time data means using the numbers that were actually knowable on a given date, not today's restated version. Skip it and your research quietly looks smarter than it was.
- AI & Finance
The Hardest Part of AI in Finance Is Not the Model. It Is the Data.
In financial AI, the model is fast becoming a commodity. The durable edge lives in disciplined data work: units, restatements, point-in-time correctness.
- AI & Finance
Can AI Actually Read a Balance Sheet? Where LLMs Break on Financial Statements
Language models are strong at prose and weak at accounting. Here is exactly where they break on real filings, and what makes machine reading of statements reliable.
- Education
Lookahead Bias, Explained: The Silent Killer of Stock Backtests
Lookahead bias is when a backtest uses information it could not have known at the time. It quietly inflates results, and point in time data is the only real fix.
- AI & Finance
Why AI Investing Apps Keep Getting Indian Stocks Wrong
Most AI investing tools are built for clean global data. Indian equities are full of local quirks that make those tools confidently wrong. Here is why.
- AI & Finance
Why ChatGPT Hallucinates Financial Numbers, and How to Catch It
General chatbots predict plausible text, they do not look up facts, so they invent revenue and profit numbers. Here is why, and how to catch it.
- Methodology
Data Quality Beats Model Quality: A Year Reading Indian Filings
After a year building AI to read Indian company filings, the biggest gains came from boring data discipline, not from a better model. Here is what actually moved the needle.