Fraud Fundamentals
Before you pick fraud tools or write rules, understand the concepts that drive every decision in fraud management. These two pages cover the "why" behind fraud strategy.
What You'll Learn
| Page | What It Covers | Read This If... |
|---|---|---|
| Economics of Fraud | The true cost of fraud (direct losses, false positives, operational costs, customer impact) | You need to justify fraud prevention spend or understand why blocking too much is also expensive |
| Risk Appetite | How to balance fraud loss tolerance, customer friction, conversion targets, and regulatory requirements | You're deciding how aggressive your fraud rules should be or where to set your score thresholds |
The Core Trade-off
Every fraud decision involves the same trade-off: block more fraud vs. block fewer good customers. Tighten your rules and you catch more fraud but reject more legitimate sales. Loosen them and you approve more revenue but accept more losses.
The right balance depends on your business. A $5 digital good has different economics than a $500 physical product. A business at 0.1% fraud rate makes different choices than one at 1.5%.
These two pages give you the framework to find your balance:
- Start with Economics to understand what fraud actually costs (it's more than just the transaction amount)
- Then read Risk Appetite to set your thresholds based on your specific business model
Key Principles
- Not all fraud is created equal – First-party (customer abuse) and third-party fraud (stolen cards) require different responses
- Prevention has costs too – False positives hurt revenue and customer relationships (see Economics)
- Speed matters – Early detection limits losses; see Detection Framework
- Data is your weapon – Invest in device fingerprinting, velocity rules, and evidence collection
- 3DS is your friend – 3D Secure shifts liability for stolen card fraud to issuers
Related Topics
- Fraud Types - Taxonomy of fraud patterns
- Detection Methods - How to identify fraud
- Prevention Strategies - How to stop fraud before it happens
- Chargebacks - When fraud becomes disputes
- Fraud Metrics - Measuring fraud performance
- Network Programs - Threshold consequences
- 3D Secure - Authentication trade-offs
- Rules vs ML - Detection approaches
- Benchmarks - Industry comparisons