AI in Finance
Where machines genuinely help — and where judgement must stay human.
A practical hub on artificial intelligence in professional finance workflows. No hype, no miracle claims — only what works, what fails, and how to tell the difference.
AI in Financial Modelling
Formula drafting, structure suggestions and rapid prototyping — with human review as the non-negotiable final step.
AI in Equity Research
Summarising filings, extracting data points and monitoring disclosures at a scale no analyst team can match manually.
AI-assisted Valuation
Accelerating comps gathering, normalisation and scenario construction — while assumptions and judgement remain human.
AI in M&A
Target screening, document review and synergy benchmarking across large data rooms in a fraction of the traditional time.
AI in Due Diligence
Contract review, anomaly detection and cross-referencing thousands of pages — with findings verified against source documents.
AI in Financial Statement Analysis
Pattern recognition across years of statements: margin drift, working capital anomalies, accounting policy shifts.
AI-assisted Model Auditing
Automated checks for broken links, inconsistent signs and formula outliers — a tireless first pass before human review.
AI in Research Automation
Pipelines that collect, clean and structure financial data so analysts spend time on judgement rather than data entry.
AI-driven Document Review
Reading and classifying offering circulars, annual reports and credit agreements with citation back to source.
AI in Transaction Screening
Filtering large opportunity sets against strategic criteria to focus human attention where it creates most value.
Limitations & model risk
Large language models hallucinate figures, misread context and present errors with confidence. They cannot own an output, sign a model or take responsibility for a recommendation. Model risk — the risk of decisions built on flawed tools — is amplified, not removed, by AI.
The professional pattern
- AI drafts; humans verify. Every AI-assisted figure is checked by someone accountable for it.
- Sources are cited and traced back to original documents before use.
- AI outputs are stress-tested against known cases before being trusted on new ones.
- Confidential data never enters an unapproved external tool.
- The human analyst signs the work — never the machine.