Deep learning has the potential to revolutionise the insurance sector – but the challenge is how to make the artificial intelligence (AI) models auditable. Check out the latest findings on how the hype around artificial intelligence could be sowing damaging confusion. Also, read a number of case studies on how enterprises are using AI to help reach business goals around the world. You forgot to provide an Email Address. This email address doesn't appear to be valid.

Prudential's global head of AI, Michael Natusch, took to the stage at RE•WORK's Deep Learning in Finance event in London this week to explain the global financial services firm's approach to artificial intelligence (AI) – explaining which algorithm to use and when. Natusch gave a top level view of Prudential's thinking around what AI user interfaces to use in what situations and which algorithms should be applied, depending on the requirements for causation or correlation. He began by explaining the fundamentals of AI (see below image), stating that organisations need three fundamentals: data, intelligent agents (data scientists/algorithms) and user interfaces. Very clearly we need data. On top of data you need some sort of model that sits on top that makes sense of what's in that data.

AI will lead to humans losing their jobs and widespread redundancy – that's the theory, anyway. However new research has suggested that firms which are investing in smart, automated and self-teaching systems are more likely to be creating jobs with it. Researchers at Capgemini surveyed 1,000 organizations which have deployed AI-based systems and found that four out of five of them have created more jobs. Additionally, two thirds of respondents said there have been no reduction in overall jobs due to AI. Of course, it's very early days – the AI that even leading-edge businesses are using today is likely to look pretty stupid compared to the technology which will be available in five years' time.