| ▲ | fny 3 hours ago | |||||||||||||||||||||||||||||||
Isn't the point of ML that you learn these rules from the data? The right approach to me would be to use ML models to detect patterns that correspond with fraud and then evaluate them to see if any make sense. This way you might discover new hyptotheses. | ||||||||||||||||||||||||||||||||
| ▲ | bob1029 3 hours ago | parent [-] | |||||||||||||||||||||||||||||||
Anything that can't be explained and iterated deterministically is too risky for the business of declining financial transactions. Human analysts need to be able to explain to compliance in a single 5 minute email why a specific transaction was declined, and most importantly, what could have been done differently to avoid the adverse decision. Fixing one problem with ML often creates two new problems that aren't quite obvious yet. SQL tends to have fewer surprises with regard to regressions and unexpected side effects as things change over time. | ||||||||||||||||||||||||||||||||
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