Editorial note: Examples on this page are illustrative unless a company, study or source is specifically named. The aim is to show realistic workflows without presenting invented scenarios as case studies.
Where AI is genuinely useful right now
- Fraud detection. Spotting unusual transaction patterns in real time, at a scale no manual review team could match.
- Personal budgeting tools. Categorising spending and flagging unusual charges automatically.
- Customer service. Handling balance checks, card issues, and common questions instantly.
- Credit risk modelling. Analysing a broader set of data points than traditional credit scoring, within regulatory limits.
Illustrative scenario: most major banks now use AI-driven fraud detection that flags unusual transactions in real time — a system most cardholders have experienced firsthand as a "was this you?" text alert.
Where to be careful
- Lending discrimination. Both the US (under fair lending laws) and UK (under FCA rules) heavily regulate AI use in credit and lending decisions — a biased model can create real legal exposure.
- Never use consumer AI chatbots for account-specific advice. General-purpose AI tools don't have your actual account data and can produce confidently wrong financial guidance.
- Explainability for denials. A declined loan or credit application typically requires an explainable reason — a black-box AI score alone is often not sufficient to meet regulatory requirements.
Worth knowing: if a financial AI tool is making decisions about your money, you generally have a right to ask what factors it's actually using — this is worth asking your bank or lender directly.
Getting started: For personal use, budgeting apps with built-in AI categorisation are the lowest-risk starting point — just avoid ever asking a general AI chatbot for account-specific financial advice, since it has no access to your actual accounts.
Speed up reading and reconciliation, not financial judgment
Finance work contains many structured, repetitive tasks, but it also contains decisions where a plausible mistake can be expensive. I would use AI to explain variance notes, classify text, summarise public filings or prepare reconciliation questions—not to approve a payment, investment or compliance decision by itself.
A practical test: A useful pilot uses historical, non-sensitive data where the correct result is already known. That makes it possible to measure whether AI is actually helping rather than merely producing confident-looking output.
A prompt template to adapt
This is a template, not a tested result. Replace the brackets with your own details:
"Summarise this report in plain language for a non-specialist: [report]. List the five numbers that matter most, say where in the document each one comes from, and flag anything you are unsure of."
What to check before you rely on it
- Recalculate every figure yourself. Language models can make arithmetic and transcription errors that look convincing.
- Check how any tool handles client or account data before using it, and follow your organisation’s rules on regulated information.
- Keep a person accountable for decisions on credit, trading, advice and compliance. AI output is not financial advice.
- Keep a record of what was asked and what was used, in case a decision has to be explained later.
For the habits behind these checks, see Fact-Checking AI Output and Privacy & Data When Using AI.
Frequently asked questions
Can I trust an AI chatbot's investment advice?
No — treat any specific investment or account guidance from a general-purpose AI chatbot as unreliable, since it doesn't have your actual financial data and isn't a licensed advisor. Use it for general concepts, not decisions.
Why was I denied credit if an AI model was involved in the decision?
Regulations in both the US and UK generally require an explainable reason for a credit denial — you're typically entitled to ask for the specific factors behind the decision, even when a model contributed to it.