Machine Learning is rarely sufficient, in isolation, to segment your target population into (for example) low risk and high risk groups, especially in highly imbalanced fraud-like problems. This is exacerbated when the emphasis is on identifying the negligible risk population, rather than the potential frauds.
Following on from Lee Brown’s blog, in this post I want to talk about how in an assurance scoring framework you can combine multiple data science techniques to identify the negligible risk cohort that can be fast tracked through processing, allowing investigators to focus their resources elsewhere.
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