back-testing
What It Means
Back-testing is like checking your AI model's report card by comparing its predictions against what actually happened in the real world. You test the model on data it has already seen during training (in-sample) and on completely new data it has never encountered (out-of-time) to see how well it would have performed.
Why Chief AI Officers Care
Back-testing is essential for proving to regulators, executives, and stakeholders that your AI systems actually work as promised before deploying them in production. It helps identify model weaknesses, reduces the risk of costly failures, and provides the evidence needed for regulatory compliance in industries like finance and healthcare.
Real-World Example
A bank's AI fraud detection system is back-tested by running it against the past year's transaction data to see if it would have correctly flagged the fraudulent transactions that actually occurred, while missing as few legitimate transactions as possible. This helps validate the system before it goes live with real customer accounts.
Common Confusion
People often confuse back-testing with regular model validation during development, but back-testing specifically focuses on testing the model's real-world performance over time periods that simulate actual deployment conditions.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, back-testing validates clinical prediction models by testing them against historical patient data to e...
Finance: In finance, back-testing validates trading algorithms, risk models, and credit scoring systems by running them against h...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"A form of outcomes analysis that involves the comparison of actual outcomes with modeled forecasts during a development sample time period (in-sample back-testing) and during a sample period not used in model development (out-of-time back-testing), and at an observation frequency that matches the forecast horizon or performance window of the model."Source: Comptroller_Office
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