The biggest problem with ML is that it takes time for it to react to the new fraud patterns/schemes.
The biggest problem with ML is that it takes time for it to react to the new fraud patterns/schemes. While rules engines have their limitations, they also have one big benefit: they allow to block recognized fraud really fast. So the actual question here is whether the ML for detecting fraud is better than a flexible rules engine and goods analysts/statisticians. In my personal experience, statistical analysis and anomalies detection effectively handles majority of the fraud. I would be interested to see a more detailed analysis of Sift Science performance with some numbers for false positives/false negatives for example, though (of course) it is probably proprietary information.
Posted by lsh123 on May 14, 2014, 11:45 PM
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- I mean that's the problem though.
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Raise real-world problems, find people facing the same issue, and discuss solutions.9,500 issues0 comments
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I mean that's the problem though.
I've always believed the opposite: get conditionals deep in your code so that the higher level control flow is regular.
The biggest problem with AI isn't being able to do things.
I am a 3D printing maker type nerd (I make my own tools for other creative tasks, rather than as their own outcomes) and I saw an incredi...
Of course not, those are clearly scams.
Claude makes more detailed plans that seem better if you just skim them, but when analyzed, has a lot of errors, usually.
Effectively everyone is building the same tools with zero quantitative benchmarks or evidence behind the why / ideas … this entire s...
> creating and operating a small company in Germany is a nightmare To be fair, I think the problem operating in Germany is its federated...
The biggest problem with ML is that it takes time for it to react to the new fraud patterns/schemes.
The biggest problem with ML is that it takes time for it to react to the new fraud patterns/schemes. While rules engines have their limitations, they also have one big benefit: they allow to block recognized fraud really fast. So the actual question here is whether the ML for detecting fraud is better than a flexible rules engine and goods analysts/statisticians. In my personal experience, statistical analysis and anomalies detection effectively handles majority of the fraud. I would be interested to see a more detailed analysis of Sift Science performance with some numbers for false positives/false negatives for example, though (of course) it is probably proprietary information.
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