Category
AI & Finance
26 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
Why Every Investment Team Will Have an AI Operating System
Research teams will move from scattered point tools to one shared, always-on layer: clean sourced data, queryable documents, and continuous monitoring, so analysts spend their time on judgement.
- AI & Finance
Financial Modelling with AI: What It Does and What Stays Human
AI speeds up the mechanical parts of financial modelling (gathering inputs, spreading history, checking consistency, drafting), while assumptions, judgement, and the forecast stay with you.
- AI & Finance
From Reading Documents to Asking Questions
Research is shifting from reading whole filings front to back to interrogating them with specific questions and getting sourced answers, which changes where an analyst spends time and attention.
- AI & Finance
How AI Compresses a Week of Research Into an Hour
AI collapses the grunt work of primary research, gathering, reading, and spreading numbers, from days to minutes. The judgement, the part that decides the outcome, still takes a human.
- AI & Finance
Investing Before AI and After AI: How the Research Day Actually Changes
Before AI, an analyst's day was manual reading and hand-spreading numbers. After AI, the reading is delegated and the human spends the day on judgement.
- 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 Death of Ctrl+F in Annual Reports
Keyword search finds strings, not meaning. It misses synonyms, ignores context, and cannot answer a question, which is why reading filings is shifting from searching words to asking questions.
- AI & Finance
The Death of the Static Research Report
A research report is a snapshot that starts decaying the day it is filed. It is being replaced by living, queryable research that updates itself as the facts change.
- 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 Citations Are Non-Negotiable in Financial AI
Every number a financial AI reports must link to the source document. An unsourced but plausible figure is worse than no answer, because it invites a costly error.
- AI & Finance
Why Deterministic Forecasting Beats LLM Guesses
A forecast used for capital must be reproducible and auditable. A language model's free-form guess is neither, which is why serious forecasts come from an explicit method, not a prompt.
- AI & Finance
Why Every Analyst Will Have an AI Associate
An AI associate does the tireless first pass, pulling numbers and reading every page, while the human analyst keeps the judgement, conviction, and accountability.
- 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
Why Research Coverage Is Becoming Obsolete
A fixed coverage list exists because analyst time was scarce and expensive. When reading and monitoring get cheap, that rationing breaks, and the narrow list of names a team follows stops making sense.
- AI & Finance
AI for Equity Research: A Practical Guide
AI speeds up equity research by summarising filings, extracting data, monitoring events, and drafting notes, as long as you verify every figure against the source.
- 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
The AI Research Analyst Is Here: What Actually Works and What Is Just Marketing
AI can already read filings, extract data, and monitor events at scale. It cannot pick winners on command. Here is how to tell the tools apart before you buy.
- AI & Finance
Can AI Predict Company Earnings? Separating Hype From Reality
AI can read filings and model earnings faster than any human, but it cannot see the future. Here is what the technology genuinely does, and where its limits are hard.
- 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.
- AI & Finance
GPT vs Claude vs Gemini on Company Filings: Why Model Benchmarks Mislead in Finance
Public LLM leaderboards rank general reasoning, not filing work. In finance the gap that decides quality lives in data handling, not raw model IQ.
- 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.
- AI & Finance
Why Earnings Call Transcripts Break Search, and What AI Must Do Instead
Transcripts hide their most important signals from keyword and even semantic search. The fix is structured extraction of management commentary, tracked over time.
- AI & Finance
Why RAG Alone Fails for Equity Research
Retrieval-augmented generation reads filings like prose. Equity research lives in tables, footnotes and vintages, where one wrong digit is a wrong answer.
- AI & Finance
AI Will Not Replace the Analyst. It Will Replace the Grunt Work.
The threat to equity research is not the analyst's judgement, it is the hours spent gathering filings and re-keying numbers. AI is coming for the grunt work first.