Fraud Detection with Machine Learning
Fraudsters change tactics constantly, so fixed rules struggle to keep up. Machine learning can spot suspicious patterns, but it must be tuned carefully to avoid blocking genuine customers.
Here is how it works and the trade-offs involved.
How It Helps
Models learn what normal activity looks like and flag transactions that deviate — unusual amounts, locations or speeds — often catching new schemes that rules would miss.
The Balancing Act
- False positives: blocking honest customers annoys them and loses sales.
- False negatives: missing real fraud costs money.
- Tuning shifts the balance; the right point depends on your risk appetite.
Keeping People Involved
High-risk flags should pause for human review rather than automatically blocking, so a person can make the final call on borderline cases.
Frequently Asked Questions
Will it stop all fraud?
No. It reduces fraud and reacts to new patterns faster than rules, but determined fraudsters still get through some of the time.
If you need a hand with any of this, your Progressive Robot delivery team is ready to help. Raise a ticket from the Support area of your client portal or speak to your account manager and we will guide you through the next steps.