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causaLens CEO explains Causal AI in Global Banking & Finance Review

This article explains how Causal AI is already being used to predict and optimise many businesses but, as the examples outlined above demonstrate, it has the potential to improve operations across a range of industries and, in time, could come to play a role across all of society.

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In this feature on Causal AI, causaLens CEO Darko Matovski explains that traditional machine learning is unable to adapt to environmental changes. In the case of predictions, they will severely overfit to the historical data. This is particularly true in the world of finance – the ultimate dynamic system.

Matovski outlines how enabling algorithms to understand causality improves generalisation: Causal AI makes more accurate predictions in complex systems such as those found in financial services.

In practice, a causal AI platform will retain the advantages of comprehensive automation – one of the key benefits of machine learning – allowing thousands of datasets to be cleaned, sorted and monitored simultaneously. Unlike traditional approaches, however, it combines this data with causal models and truly explainable insights – traditionally the sole province of domain experts.

Read the full article here.

“The oil and gas industry could enjoy savings of at least $200bn by optimising transportation and storage, and more accurately predicting supply and demand. And more than $500bn in food waste could be saved each year if we could better predict microclimate and demand.”

Darko Matovski in Global Banking & Finance Review

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