Definition
AI in finance is used to detect fraud, score credit risk, automate document checks, and support trading and customer service. Because it affects people's money and access to loans, it's closely regulated.
In simple words
When your card is blocked after a strange purchase, a model flagged it in milliseconds. When a loan is approved in minutes, a model likely scored your risk.
Why it matters
It's one of the largest users of ML, and an excellent lesson on imbalanced data and fairness, because fraud is rare and loan decisions can be biased.
How it works
| Use | Technique | Key challenge |
|---|---|---|
| Fraud detection | Anomaly detection, boosting | Fraud is rare, and fraudsters adapt |
| Credit scoring | Logistic regression, boosting | Must be fair and explainable |
| Document checks | OCR, LLMs | Accuracy and privacy |
| Trading | Time series models | Markets change, and overfitting is easy |
Try it yourself
Run the bank's fraud check
Slide to decide when the bank should step in. Watch all three numbers.
Should this payment be flagged?
You are the fraud model. Fraud systems look for what is unusual for this person. A flag means "check this", not "this is fraud".
Weekly groceries at the usual store
A card used in another country ten minutes after a purchase in Pune
Ten large UPI payments to a new account in five minutes at 3 am
₹40 for tea at the shop near home, as on most days
The same rent payment on the same date as every month
Someone who only ever spends small amounts suddenly buys a ₹90,000 phone online
Common mistakes
- Using accuracy for fraud. With 0.1% fraud, a model that flags nothing is 99.9% accurate.
- Believing a backtest that looks perfect. It has very likely overfit.
Check yourself
Summary
- Fraud, credit, documents and trading are the main uses.
- Imbalance and drift make fraud hard.
- Fairness and explanation are required.
Finished reading?