Open Banking: Multi-Layered Self-Calibrating Models
A good example of effective use of self-calibrating models is for open banking, where labeled historical data is not yet available and the evolution of open banking in terms of adoption is still unclear.”
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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