Before reaching for machine learning, ask whether a few rules would do the job.
Rules-Based Systems
A person writes explicit logic: if the invoice total exceeds $10,000, require a second approval. Rules are transparent, predictable, easy to audit and need no training data. They work best when the logic is known, stable and fairly simple.
Machine Learning
The system learns the logic from examples. It shines when the rules are too complex or fuzzy to write down — recognising a face, detecting subtle fraud patterns, understanding free text — and when there is enough representative data.
Comparing Them
| Rules | Machine learning | |
|---|---|---|
| Needs data | No | Yes |
| Handles fuzzy patterns | Poorly | Well |
| Explainability | High | Varies |
| Maintenance | Rules grow brittle over time | Needs retraining and monitoring |
| Predictability | High | Probabilistic |
Using Both Together
The best systems often combine them:
- rules for hard constraints and regulatory requirements;
- machine learning for scoring and ranking;
- rules to route uncertain predictions to a person.
A Sensible Order
Start with a simple rule or heuristic as a baseline. If it performs well enough, you may not need machine learning at all. If it doesn't, the baseline tells you how much better a model must be to be worth the added complexity.