Difficulties in accessing high-quality historical data or anticipated systematic shifts in data are barriers to the development of supervised machine learning (ML) models. This challenge provides a rationale to create models that are able to learn patterns and variances online as the data is streamed. These models are known as self-calibrating models, and they can be used for outlier detection, which is often a strong proxy for financial crime events.
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