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.”

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White Paper

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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