The title says it all - machine learning and rule-based systems are complementary tools, not mutually exclusive.
I have prior experience applying both to fraud detection, and for fraud detection, there are lots of regulations that are actionable as hard and fast rules running in an expert system.
There are also patterns of fraud that are not obvious, and data mining techniques along with machine learning are incredibly useful for detecting them.
More importantly, if you use a rule-based system first you can often use the output (for example, a rule-based score for a given input case) as an input into machine learning. So, for example, the rule-based system is used to help classify concrete cases, whereas the machine learning might help classify more "gray area" cases based on the hard and fast rules (depending on how you configure your algorithms).
I have prior experience applying both to fraud detection, and for fraud detection, there are lots of regulations that are actionable as hard and fast rules running in an expert system.
There are also patterns of fraud that are not obvious, and data mining techniques along with machine learning are incredibly useful for detecting them.
More importantly, if you use a rule-based system first you can often use the output (for example, a rule-based score for a given input case) as an input into machine learning. So, for example, the rule-based system is used to help classify concrete cases, whereas the machine learning might help classify more "gray area" cases based on the hard and fast rules (depending on how you configure your algorithms).