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Hawk

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AI can significantly reduce your false positives in AML. How? We explain the difference between a rules-only approach and AI. The Rules-Based Approach: > Consider three rules, each having a 10% false positive rate, deployed to detect suspicious behavior > When we apply these rules, the total false positive rate is 30% > The rate is high because all the rules act together, and they don't choose any specific type of behavior to look at. The same rule applies to every single customer. The AI Approach: > AI generates a large set of fine-grain rules that look for specific combinations of behavior > AML investigators can tailor AI much more precisely to what a particular customer does > For example, with AI we can apply five rules, each with two conditions having a false positive rate of 10% > The rules only fire if both conditions are met, resulting in a false positive rate of 1% per rule > When we combine the five rules, we get an overall false positive rate of 5% > In this scenario, we’ve applied more rules and still reduced the false positive rate significantly Read the full article that summarizes the examples and how AI reduces false positives: https://lnkd.in/dfTRDDS9

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